



@article {2842,
	title = {Automatic lane segmentation in TLC images using the continuous wavelet transform},
	journal = {Computational and Mathematical Methods in Medicine},
	volume = {2013},
	year = {2013},
	note = {Export Date: 12 December 2013Source: ScopusArt. No.: 218415},
	month = {2013///},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84885671185\&partnerID=40\&md5=dd125f056cd63a76368b0ad6db6836c6},
	author = {Moreira, B. and Sousa, A. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@article {2855,
	title = {Automatic localization of the optic disc by combining vascular and intensity information},
	journal = {Computerized Medical Imaging and Graphics},
	volume = {37},
	year = {2013},
	note = {Export Date: 12 December 2013Source: Scopus},
	month = {2013///},
	pages = {409 - 417},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84888008484\&partnerID=40\&md5=10373bbd299a0e0b2a6890fe7fb1a69a},
	author = {Mendon{\c c}a, A.M. and Sousa, A. and Mendon{\c c}a, L. and Campilho, A.}
}
@book {2197,
	title = {Automatic lane detection in chromatography images},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {7325 LNCS},
	year = {2012},
	note = {Conference code: 91199Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-31298-4_22Language of Original Document: EnglishCorrespondence Address: Moreira, B.M.; Instituto de Engenharia Biom{\'e}dica, Universidade Do Porto, Rua Roberto Frias, s/n, 4200-465 Porto, Portugal; email: moreira@fe.up.ptReferences: Fried, B., Sherma, J., (1999) Thin-Layer Chromatography, , Marcel Dekker, New York;Rodrigues, L.G., Ferraz, M.J., Rodrigues, D., Pais-Vieira, M., Lima, D., Brady, R.O., S{\'a}-Miranda, M.C., Neurophysiological behavioral and morphological abnormalities in the Fabry knockout mice (2009) Neurobiology of Disease, 33 (1), pp. 48-56; 
Elstein, D., Altarescu, G., Beck, M., (2010) Fabry Disease, , Springer, Heidelberg; 
Houck, M.M., Siegel, J.A., (2010) Fundamentals of Forensic Science, , Elsevier, Oxford; 
Machado, A.M.C., Campos, M.F.M., Siqueira, A.M., De Carvalho, O.S.F., An iterative algorithm for segmenting lanes in gel electrophoresis images (1997) Proc. X Brazilian Symposium on Computer Graphics and Image Processing, pp. 140-146. , October; 
Elder, J.K., Southern, E.M., Computer-aided analysis of one dimensional restriction fragment gels (1987) Nucleic Acid and Protein Sequence Analysis - A Practical Aproach, pp. 165-172. , Bishop, M.J., Rawlings, C.J. (eds.) IRL Press, Oxford; 
Bajla, I., Holl{\"a}nder, I., Fluch, S., Burg, K., Koll{\'a}r, M., An alternative method for electrophoretic gel image analysis in the GelMaster software (2005) Computer Methods and Programs in Biomedicine, 77, pp. 209-231; 
Lin, C., Ching, Y., Yang, Y., An Automatic Method to Compare the Lanes in Gel Electrophoresis (GE) Images (2007) IEEE Transaction on Information Technology in Biomedicine, 11 (2), pp. 179-189; 
Akbari, A., Fritz, A., Jackobsen, K.S., Automatic lane detection and separation in one dimensional gel images using continuous wavelet transform (2010) The Royal Society of Chemistry, Analytical Methods, 2, pp. 1360-1371; 
Sousa, A.V., Aguiar, R.L., Mendon{\c c}a, A.M., Campilho, A.C., Automatic Lane and Band Detection in Images of Thin Layer Chromatography (2004) LNCS, 3212, pp. 158-165. , Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. Springer, Heidelberg; 
Sotaquira, M., On the use of distance maps in the analysis of 1D DNA gel images (2009) ICDIP 2009: Proceedings of the International Conference on Digital Image Processing, pp. 172-176. , IEEE Computer Society; 
Barrantes, P., Alvarado, P., Lane Detection on Gel Electrophoresis Images using Active Shape Models (2011) Proc. of the Conference on Technologies for Sustainable Development, TSD 2011, pp. 43-46. , February; 
Mendon{\c c}a, A.M., Sousa, A.V., S{\'a}-Miranda, M.C., Campilho, A.C., Automatic segmentation of chromatographic images for region of interest delineation (2011) Proc. SPIE, 7962, pp. 79623B1-79623B7; 
Chau, F., Liang, Y., Gao, J., Shao, X., (2004) Chemometrics - from Basics to Wavelet Transform, , John Wiley \& Sons, New Jersey; 
Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T., (1992) Numerical Recipes in C: The Art of Scientific Computing, , Syndicate of the University of Cambridge; 
Soille, P., Jones, C.D., Smith, A.B., Roberts, E.F., (2004) Morphological Image Analysis - Principles and Applications, , Springer; 
Visa, S., Ralescu, A., Learning Imbalanced and Overlapping Classes using Fuzzy Sets Workshop on Learning from Imbalanced Datasets II, ICML (2003)
},
	month = {2012///},
	pages = {180 - 187},
	address = {Aveiro},
	abstract = {This paper proposes a method for automating the detection of lanes in Thin-Layer Chromatography images. Our approach includes a preprocessing step to detect the image region of interest, followed by background estimation and removal. This image is then projected onto the horizontal direction to integrate the information into a one-dimensional profile. A smoothing filter is applied to this profile and the outcome is the input of the lane detection process, which is performed in three phases. The first one aims at obtaining an initial set of candidate lanes that are further validated or removed in the second phase. The last phase is a refinement step that allows the inclusion of lanes that are not clearly distinguishable in the profile and that were not included in the initial set. The method was evaluated in 66 chromatography images and achieved values of recall, precision and F                        β -measure of 97.0\%, 99.4\% and 98.2\%, respectively. {\textcopyright} 2012 Springer-Verlag.
},
	keywords = {Background estimation, Bio-medical image processing, Biomedical image processing, Chromatography, Chromatography images, Computer aided diagnosis, Image analysis, Image regions, Lane detection, Pre-processing step, Refinement step, Second phase, Smoothing filters, Three phasis},
	isbn = {03029743 (ISSN); 9783642312977 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84864120489\&partnerID=40\&md5=1e51865766ec865c2127c5b672f97b8f},
	author = {Moreira, B.M. and Sousa, A.V. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@book {2198,
	title = {Automatic localization of the optic disc in retinal images based on the entropy of vascular directions},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {7325 LNCS},
	year = {2012},
	note = {Conference code: 91199Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-31298-4_50Language of Original Document: EnglishCorrespondence Address: Mendon{\c c}a, A.M.; Instituto de Engenharia Biom{\'e}dica, Universidade Do Porto, Rua Roberto Frias, s/n, 4200-465 Porto, Portugal; email: amendon@fe.up.ptReferences: Jelinek, H., Cree, M., (2010) Automated Image Detection of Retinal Pathology, , CRC Press, Taylor \& Francis Group, Boca Raton;Couper, D.J., Klein, R., Hubbard, L.D., Wong, T.Y., Sorlie, P., Cooper, L.S., Brothers, R.J., Nieto, F.J., Reliability of Retinal Photography in the Assessment of Retinal Microvascular Characteristics: The Atherosclerosis Risk in Communities Study (2002) American Journal of Ophthalmology, 133 (1), pp. 78-88; 
Sinthanayothin, C., Boyce, J.F., Cook, H.L., Williamson, T.H., Automated localization of the optic disc, fovea, and retinal blood vessels from digital colour fundus images (1999) Br. J. Ophthalmol., 83, pp. 902-910; 
Aquino, A., Geg{\'u}ndez-Arias, M.E., Marin, D., Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection and Feature Extraction Techniques (2010) IEEE Trans. Medical Imaging, 29 (11), pp. 1860-1869; 
Aquino, A., Geg{\'u}ndez-Arias, M.E., Marin, D., Automated Optic Disc Detection in Retinal Images of Patients with Diabetic Retinopathy and Risk of Macular Edema (2012) Int. J. of Biological and Life Sciences, 8 (2), pp. 87-92; 
Lalonde, M., Beaulieu, M., Gagnon, L., Fast and Robust Optic Disc Detection Using Pyramidal Decomposition and Hausdorff-based Template Matching (2001) IEEE Trans. Medical Imaging, 20 (11), pp. 1193-1200; 
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Abdel-Ghafar, R.A., Morris, T., Progress towards automated detection and characterization of the optic disc in glaucoma and diabetic retinopathy (2007) Medical Informatics and the Internet in Medicine, 32 (1), pp. 19-25; 
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Foracchia, M., Grisan, E., Ruggeri, A., Detection of Optic Disc in Retinal Images by Means of a Geometrical Model of Vessel Structure (2004) IEEE Trans. Medical Imaging, 23 (10), pp. 1189-1195; 
Haar, F., (2005) Automatic Localization of the Optic Disc in Digital Colour Images of the Human Retina, , Msc Thesis, University of Utrecht; 
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Niemeijer, M., Abr{\`a}moff, M.D., Van Ginneken, B., Fast detection of the optic disc and fovea in color fundus photographs (2009) Medical Image Analysis, 13, pp. 859-870; 
Xu, X., Gravin, M.K., Abr{\`a}moff, M.D., Reinhardt, J.M., Simultaneous Automatic Detection of the Optic Disc and Fovea on Fundus Photographs (2011) Proc. SPIE Medical Imaging 2011: Image Processing, 7962, pp. 7962T. , Dawant, B.M., Haynor, D.R. (eds.) SPIE; 
http://www.isi.uu.nl/Research/Databases/DRIVEhttp://www.ces.clemson.edu/~ahoover/stare, The STARE projectMendon{\c c}a, A., Campilho, A., Segmentation of Retinal Blood Vessels by Combining the Detection of Centerlines and Morphological Reconstruction (2006) IEEE Trans. Medical Imaging, 25 (9), pp. 1200-1213; 
Chaudhuri, S., Chattererjee, S., Katz, N., Glodbaum, M., Detection of blood Vessels in Retinal Images using Two-Dimensional Matched Filters (1989) IEEE Trans. Medical Imaging, 28 (3), pp. 263-269
},
	month = {2012///},
	pages = {424 - 431},
	address = {Aveiro},
	abstract = {This paper proposes an automatic method for estimating the location of the optic disc in color images of the retina. The proposed methodology is founded in a new concept, the entropy of vascular directions, which proved to be a reliable measure for assessing the convergence of vessels around an image point. To improve the robustness of the method, the search for the maximum value of entropy is restricted to image areas with high intensity. This new method was evaluated in two publicly available databases, containing both normal and pathological images, and was able to obtain a valid location for the optic disc in 115 out of the 121 images of the two datasets. {\textcopyright} 2012 Springer-Verlag.},
	keywords = {Automatic localization, Automatic method, Color images, Data sets, entropy, High intensity, Image analysis, Image points, Maximum values, Ophthalmology, optic disc, Pathological images, Retinal image, Retinal image analysis, vessel segmentation},
	isbn = {03029743 (ISSN); 9783642312977 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84864128693\&partnerID=40\&md5=faf6f5351e81f8c595247728be684d95},
	author = {Mendon{\c c}a, A.M. and Cardoso, F. and Sousa, A.V. and Campilho, A.}
}
@article {2206,
	title = {Gradient convergence filters and a phase congruency approach for in vivo cell nuclei detection},
	journal = {Machine Vision and ApplicationsMach Vision Appl},
	volume = {23},
	year = {2012},
	note = {Cited By (since 1996): 1Export Date: 21 November 2012Source: ScopusCODEN: MVAPEdoi: 10.1007/s00138-012-0407-7Language of Original Document: EnglishCorrespondence Address: Quelhas, P.; INEB-Instituto de Engenharia Biom{\'e}dica, Oporto, Portugal; email: quelhas@fe.up.ptReferences: Bleau, A., Leon, L.J., Watershed-based segmentation and region merging (2000) Computer Vision and Image Understanding, 77 (3), pp. 317-370. , DOI 10.1006/cviu.1999.0822;Bresson, X., Vandergheynst, P., Thiran, J., A priori information in image segmentation: Energy functional based on shape statistical model and image information (2003) Proceedings of International Conference on Image Processing, pp. 428-428. , ICIP; 
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Byun, J., Verardo, M.R., Sumengen, B., Lewis, G.P., Manjunath, B.S., Fisher, S.K., Automated tool for the detection of cell nuclei in digital microscopic images: Application to retinal images (2006) Mol. Vis., 12, pp. 949-960; 
Campilho, A., Garcia, B., Toorn, H.V.D., Wijk, H.V., Campilho, A., Scheres, B., Time-lapse analysis of stem-cell divisions in the Arabidopsis thaliana root meristem (2006) Plant Journal, 48 (4), pp. 619-627. , DOI 10.1111/j.1365-313X.2006.02892.x; 
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Chen, Y., Quelhas, P., Campilho, A., Low frame rate cell tracking: A delaunay graph matching approach (2011) Proceedings of the IEEE International Conference on Biomedical Imaging (ISBI), pp. 1-4; 
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},
	month = {2012///},
	pages = {623 - 638},
	abstract = {Computational methods used in microscopy cell image analysis have largely augmented the impact of imaging techniques, becoming fundamental for biological research. The understanding of cell regulation processes is very important in biology, and in particular confocal fluorescence imaging plays a relevant role for the in vivo observation of cells. However, most biology researchers still analyze cells by visual inspection alone, which is time consuming and prone to induce subjective bias. This makes automatic cell image analysis essential for large scale, objective studies of cells. While the classic approach for automatic cell analysis is to use image segmentation, for in vivo confocal fluorescence microscopy images of plants, such approach is neither trivial nor is it robust to image quality variations. To analyze plant cells in in vivo confocal fluorescence microscopy images with robustness and increased performance, we propose the use of local convergence filters (LCF). These filters are based in gradient convergence and as such can handle illumination variations, noise and low contrast.We apply a range of existing convergence filters for cell nuclei analysis of the Arabidopsis thaliana plant root tip. To further increase contrast invariance, we present an augmentation to local convergence approaches based on image phase information. Through the use of convergence index filters we improved the results for cell nuclei detection and shape estimation when compared with baseline approaches. Using phase congruency information we were able to further increase performance by 11\% for nuclei detection accuracy and 4\% for shape adaptation. Shape regularization was also applied, but with no significant gain, which indicates shape estimation was good for the applied filters. {\textcopyright} 2011 Springer-Verlag.},
	keywords = {Arabidopsis thaliana, Biological research, Cell analysis, Cell images, cell nucleus, Cell regulation, Cell segmentation, Cells, Confocal fluorescence imaging, Confocal fluorescence microscopy, Convergence filters, cytology, Detection accuracy, Estimation, Fluorescence microscopy, Illumination variation, Image analysis, Image filtering, Image segmentation, Imaging techniques, In-vivo, Information use, Local Convergence, Local image filtering, Medical applications, Phase congruency, Phase information, Plant cells, Plant roots, Plants (botany), Quality variation, Shape adaptation, Shape estimation, Shape regularization, Visual inspection},
	isbn = {09328092 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84865374061\&partnerID=40\&md5=8e2b458028b5492266f085546a6804d9},
	author = {Esteves, T. and Quelhas, P. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@book {2202,
	title = {Hand detection and tracking using the skeleton of the blob for medical rehabilitation applications},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {7325 LNCS},
	year = {2012},
	note = {Conference code: 91199Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-31298-4_16Language of Original Document: EnglishCorrespondence Address: Gil-Jim{\'e}nez, P.; Universidad de Alcal{\'a}, Alcal{\'a} de Henares, Madrid, Spain; email: pedro.gil@uah.esReferences: Breuer, P., Eckes, C., M{\"u}ller, S., Hand Gesture Recognition with a Novel IR Time-of-Flight Range Camera-A Pilot Study (2007) LNCS, 4418, pp. 247-260. , Gagalowicz, A., Philips, W. (eds.) MIRAGE 2007. Springer, Heidelberg;Chang, C., Lin, C., (2001) LIBSVM: A Library for Support Vector Machines, , http://www.csie.ntu.edu.tw/~cjlin/libsvm, software available at; 
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G{\'o}mez-Moreno, H., Maldonado-Basc{\'o}n, S., Gil-Jim{\'e}nez, P., Lafuente-Arroyo, S., (2010) Goal Evaluation of Segmentation Algorithms for Traffic Sign Recognition, 11 (4), pp. 917-930. , July; 
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Von Hardenberg, C., B{\'e}rard, F., Bare-hand human-computer interaction (2001) Proceedings of the 2001 Workshop on Perceptive User Interfaces, PUI 2001, pp. 1-8; 
Hsieh, C.C., Liou, D.H., Lee, D., A real time hand gesture recognition system using motion history image (2010) 2010 2nd International Conference on Signal Processing Systems (ICSPS), 2, pp. V2394-V2398. , July; 
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Isaacs, J., Foo, J., Hand pose estimation for american sign language recognition (2004) Southern Symposium on System Theory, pp. 132-136; 
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Letessier, J., B{\'e}rard, F., Visual tracking of bare fingers for interactive surfaces (2004) Proceedings of the 17th Annual ACM Symposium on User Interface Software and Technology, UIST 2004, pp. 119-122. , ACM; 
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McCabe, C.S., Haigh, R.C., Ring, E.F., Halligan, P.W., Wall, P.D., Blake, D.R., A controlled pilot study of the utility of mirror visual feedback in the treatment of complex regional pain syndrome (type 1) (2003) Rheumatology, 42 (1), pp. 97-101; 
Murray, C.D., Patchick, E., Pettifer, S., Howard, T., Caillette, F., Kulkarni, J., Bamford, C., Investigating the efficacy of a virtual mirror box in treating phantom limb pain in a sample of chronic sufferers (2006) Disabil Human Dev., 5 (3), pp. 227-234; 
Nope, R., Sandra, E., Humberto Loaiza, C., Eduardo Caicedo, B., Estudio omparativo de t{\'e}cnicas para el reconocimiento de gestos por visi{\'o}n artificial (2009) Avances en Sistemas e Informtica, 5 (3); 
Oikonomidis, I., Nikolaos, K., Argyros, A.A., Efficient model-based 3d tracking of hand articulations using kinect (2011) Tracking Hand Articulations Using Kinect; 
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Ren, Z., Meng, J., Yuan, J., Zhang, Z., Robust hand gesture recognition with kinect sensor (2011) Proceedings of the 19th ACM International Conference on Multimedia, MM 2011, pp. 759-760. , ACM; 
Yoruk, E., Konukoglu, E., Sankur, B., Darbon, J., Shape-based hand recognition (2006) IEEE Transactions on Image Processing, 15 (7), pp. 1803-1815
},
	month = {2012///},
	pages = {130 - 137},
	address = {Aveiro},
	abstract = {This article presents an image processing application for hand detection and tracking using the 4-connected skeleton of the segmentation mask. The system has been designed to be used with techniques of virtual reality to develop an interactive application for phantom limb pain reduction in therapeutic treatments. One of the major contributions is the design of a fast and accurate skeleton extractor, that has proven to be faster than those available in the literature. The skeleton allows the system to precisely detect the position of all the interest points of the hand (namely the fingers and the hand center). The system, composed of both the hand detector and tracker, and the virtual reality application, can work in real-time, allowing the patient to watch the virtual image of his own hand on a screen. {\textcopyright} 2012 Springer-Verlag.},
	keywords = {blob skeleton, Hand detection, hand detection and tracking, Image analysis, Image processing applications, Interactive applications, Musculoskeletal system, phantom limb pain reduction, Therapeutic treatments, Virtual images, virtual reality},
	isbn = {03029743 (ISSN); 9783642312977 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84864140682\&partnerID=40\&md5=39ee654bd93d52a92d0be5977cff42f7},
	author = {Gil-Jim{\'e}nez, P. and Losilla-L{\'o}pez, B. and Torres-Cueco, R. and Campilho, A. and L{\'o}pez-Sastre, R.}
}
@book {2199,
	title = {Supervised content based image retrieval using radiology reports},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {7325 LNCS},
	year = {2012},
	note = {Conference code: 91199Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-31298-4_30Language of Original Document: EnglishCorrespondence Address: Ramos, J.; INEB - Instituto de Engenharia Biom{\'e}dica, Faculdade de Engenharia, Universidade Do PortoPortugalReferences: Aisen, A.M., Broderick, L.S., Winer-Muram, H., Brodley, C.E., Kak, A.C., Pavlopoulou, C., Dy, J., Marchiori, A., Automated storage and retrieval of thin-section CT images to assist diagnosis: System description and preliminary assessment (2003) Radiology, 228 (1), pp. 265-270;Akgul, C.B., Rubin, D.L., Napel, S., Beaulieu, C.F., Greenspan, H., Acar, B., Content-Based image retrieval in radiology: Current status and future directions (2010) Journal of Digital Imaging, 24 (2), pp. 208-222; 
Dreyer, K.J., Kalra, M.K., Maher, M.M., Hurier, A.M., Asfaw, B.A., Schultz, T., Halpern, E.F., Thrall, J.H., Application of recently developed computer algorithm for automatic classification of unstructured radiology reports: Validation study1 (2005) Radiology, 234 (2), pp. 323-329; 
Hersh, W.R., M{\"u}lller, H., Jensen, J.R., Yang, J., Gorman, P.N., Ruch, P., Advancing biomedical image retrieval: Development and analysis of a test collection (2006) Journal of the American Medical Informatics Association: JAMIA, 13 (5), pp. 488-496; 
Kelly, D., Teevan, J., Implicit feedback for inferring user preference: A bibliography (2003) SIGIR Forum, 37 (2), pp. 18-28; 
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Li, Q., Li, F., Shiraishi, J., Katsuragawa, S., Sone, S., Doi, K., Investigation of new psychophysical measures for evaluation of similar images on thoracic computed tomography for distinction between benign and malignant nodules (2003) Medical Physics, 30 (10), pp. 2584-2593; 
Van Rikxoort, E.M., De Hoop, B., Van De Vorst, S., Prokop, M., Van Ginneken, B., Automatic segmentation of pulmonary segments from volumetric chest CT scans (2009) IEEE Transactions on Medical Imaging, 28 (4), pp. 621-630; 
Sammon, J.W., A nonlinear mapping for data structure analysis (1969) IEEE Transactions on Computers, C-18 (5), pp. 401-409; 
Slaney, M., Weinberger, K., White, W., Learning a metric for music similarity (2008) Procedings of the 9th International Society for Music Information Retrieval Conference, pp. 313-318; 
Sluimer, I.C., Prokop, M., Hartmann, I., Van Ginneken, B., Automated classification of hyperlucency, fibrosis, ground glass, solid, and focal lesions in high-resolution CT of the lung (2006) Medical Physics, 33 (7), p. 2610; 
Yang, L., Jin, R., Mummert, L., Sukthankar, R., Goode, A., Zheng, B., Hoi, S.C., Satyanarayanan, M., A boosting framework for Visuality-Preserving distance metric learning and its application to medical image retrieval (2010) IEEE Transactions on Pattern Analysis and Machine Intelligence, 32 (1), pp. 30-44
},
	month = {2012///},
	pages = {249 - 258},
	address = {Aveiro},
	abstract = {Content based image retrieval (CBIR) is employed in medicine to improve radiologists{\textquoteright} diagnostic performance. Today effective medical CBIR systems are limited to specific applications, as to reduce the amount of medical knowledge to model. Although supervised approaches could ease the incorporation of medical expertise, its application is not common due to the scarce number of available user annotations. This paper introduces the application of radiology reports to supervise CBIR systems. The concept is to make use of the textual distances between reports to build a transformation in image space through a manifold learning algorithm. A comparison was made between the presented approach and non-supervised CBIR systems, using a Leave-One-Patient-Out evaluation in a database of computer tomography scans of interstitial lung diseases. Supervised CBIR augmented the mean average precision consistently with an increase between 3 to 8 points, which suggests supervision by radiology reports increases CBIR performance. {\textcopyright} 2012 Springer-Verlag.},
	keywords = {CBIR system, Computer tomography, Content based retrieval, Content-Based Image Retrieval, Diagnostic performance, Image analysis, Image space, Interstitial lung disease, Learning algorithms, Manifold learning algorithm, Medical computing, Medical knowledge, Radiation, Radiology, Radiology reports, User annotations},
	isbn = {03029743 (ISSN); 9783642312977 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84864135039\&partnerID=40\&md5=1aafa2e679e79b57baf3cdb50c5e4c97},
	author = {Ramos, J. and Kockelkorn, T. and Van Ginneken, B. and Viergever, M.A. and Ramos, R. and Campilho, A.}
}
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Schnittger, A., Sch{\"o}binger, U., Bouyer, D., Weinl, C., Stierhof, Y.D., H{\"u}lskamp, M., Ectopic D-type cyclin expression induces not only DNA replication but also cell division in Arabidopsis trichomes (2002) Proc. Natl. Acad. Sci. USA, 99, pp. 6410-6415; 
van Leene, J., Targeted interactomics reveals a complex core cell cycle machinery in Arabidopsis thaliana (2010) Mol. Syst. Biol., 6, p. 397; 
Vanneste, S., Cell cycle progression in the pericycle is not sufficient for SOLITARY ROOT/IAA14-mediated lateral root initiation in Arabidopsis thaliana (2005) Plant Cell, 17, pp. 3035-3050; 
Verkest, A., Manes, C.L., Vercruysse, S., Maes, S., van der Schueren, E., Beeckman, T., Genschik, P., De Veylder, L., The cyclin-dependent kinase inhibitor KRP2 controls the onset of the endoreduplication cycle during Arabidopsis leaf development through inhibition of mitotic CDKA;1 kinase complexes (2005) Plant Cell, 17, pp. 1723-1736; 
Verkest, A., Weinl, C., Inz{\'e}, D., De Veylder, L., Schnittger, A., Switching the cell cycle. Kip-related proteins in plant cell cycle control (2005) Plant Physiol., 139, pp. 1099-1106; 
Wang, H., Qi, Q., Schorr, P., Cutler, A.J., Crosby, W.L., Fowke, L.C., ICK1, a cyclin-dependent protein kinase inhibitor from Arabidopsis thaliana interacts with both Cdc2a and CycD3, and its expression is induced by abscisic acid (1998) Plant J., 15, pp. 501-510; 
Wildwater, M., Campilho, A., Perez-Perez, J.M., Heidstra, R., Blilou, I., Korthout, H., Chatterjee, J., Scheres, B., The RETINOBLASTOMA-RELATED gene regulates stem cell maintenance in Arabidopsis roots (2005) Cell, 123, pp. 1337-1349; 
Willemse, J., Kulikova, O., de Jong, H., Bisseling, T., A new whole-mount DNA quantification method and the analysis of nuclear DNA content in the stem-cell niche of Arabidopsis roots (2008) Plant J., 55, pp. 886-894; 
Winter, D., Vinegar, B., Nahal, H., Ammar, R., Wilson, G.V., Provart, N.J., An "Electronic Fluorescent Pictograph" browser for exploring and analyzing large-scale biological data sets (2007) PLoS ONE, 2, pp. e718; 
Zhou, Y., Fowke, L.C., Wang, H., Plant CDK inhibitors: Studies of interactions with cell cycle regulators in the yeast twohybrid system and functional comparisons in transgenic Arabidopsis plants (2002) Plant Cell Rep., 20, pp. 967-975; 
Zhou, Y., Niu, H., Brandizzi, F., Fowke, L.C., Wang, H., Molecular control of nuclear and subnuclear targeting of the plant CDK inhibitor ICK1 and ICK1-mediated nuclear transport of CDKA (2006) Plant Mol. Biol., 62, pp. 261-278; 
Zhou, Y., Wang, H., Gilmer, S., Whitwill, S., Fowke, L.C., Effects of co-expressing the plant CDK inhibitor ICK1 and D-type cyclin genes on plant growth, cell size and ploidy in Arabidopsis thaliana (2003) Planta, 216, pp. 604-613
},
	month = {2011///},
	pages = {641 - 660},
	abstract = {The integration of cell division in root growth and development requires mediation of developmental and physiological signals through regulation of cyclin-dependent kinase activity. Cells within the pericycle form de novo lateral root meristems, and D-type cyclins (CYCD), as regulators of the G                        1-to-S phase cell cycle transition, are anticipated to play a role. Here, we show that the D-type cyclin protein CYCD2;1 is nuclear in Arabidopsis thaliana root cells, with the highest concentration in apical and lateral meristems. Loss of CYCD2;1 has a marginal effect on unstimulated lateral root density, but CYCD2;1 is rate-limiting for the response to low levels of exogenous auxin. However, while CYCD2;1 expression requires sucrose, it does not respond to auxin. The protein Inhibitor-Interactor of CDK/Kip Related Protein2 (ICK2/KRP2), which interacts with CYCD2;1, inhibits lateral root formation, and ick2/krp2 mutants show increased lateral root density. ICK2/ KRP2 can modulate the nuclear levels of CYCD2;1, and since auxin reduces ICK2/KRP2 protein levels, it affects both activity and cellular distribution of CYCD2;1. Hence, as ICK2/KRP2 levels decrease, the increase in lateral root density depends on CYCD2;1, irrespective of ICK2/CYCD2;1 nuclear localization. We propose that ICK2/KRP2 restrains root ramification by maintaining CYCD2;1 inactive and that this modulates pericycle responses to auxin fluctuations. {\textcopyright} 2011 American Society of Plant Biologists.
},
	keywords = {Arabidopsis, Arabidopsis protein, Arabidopsis Proteins, Arabidopsis thaliana, article, ATP dependent 26S protease, cell cycle, cell cycle protein, Cell Cycle Proteins, cell nucleus, CycD2 protein, Arabidopsis, cycline, Cyclins, gene expression regulation, Gene Expression Regulation, Plant, genetics, growth, development and aging, ICK2 protein, Arabidopsis, indoleacetic acid derivative, Indoleacetic Acids, metabolism, mutation, plant root, Plant roots, proteasome, Proteasome Endopeptidase Complex},
	isbn = {10404651 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79953123085\&partnerID=40\&md5=f1355f64db2bcf9f2d881104f23005eb},
	author = {Sanz, L. and Dewitte, W. and Forzani, C. and Patell, F. and Nieuwland, J. and Wen, B. and Quelhas, P. and de Jager, S. and Titmus, C. and Campilho, A. and Ren, H. and Estelle, M. and Wang, H. and Murray, J.A.H.}
}
@book {2222,
	title = {Arabidopsis thaliana automatic cell file detection and cell length estimation},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {6754 LNCS},
	year = {2011},
	note = {Conference code: 85546Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-21596-4_1Language of Original Document: EnglishCorrespondence Address: Quelhas, P.; INEB - Divis{\~a}o de Sinal e Imagem, Instituto de Engenharia Biom{\'e}dica, Porto, PortugalReferences: Terpstra, I., Heidstra, R., Stem cells: The root of all cells (2009) Semin. Cell Dev. Biol., 20 (9), pp. 1089-1096;Bennett, T., Scheres, B., Root development-two meristems for the price of one? (2010) Curr. Top Dev. Bio., 91, pp. 67-102; 
De Smet, I., Tetsumura, T., De Rybel, B., Frey, N.F., Laplaze, L., Casimiro, I., Swarup, R., Beeckman, T., Auxin-dependent regulation of lateral root positioning in the basal meristem of arabidopsis (2007) Development, 134 (4), pp. 681-690; 
Nieuwland, J., Maughan, S., Dewitte, W., Scofield, S., Sanz, L., Murray, J.A., The d-type cyclin cycd4;1 modulates lateral root density in arabidopsis by affecting the basal meristem region (2009) Proc. Natl. Acad. Sci. U S A, 106 (52), pp. 22528-22533; 
Vanstraelen, M., Baloban, M., Da Ines, O., Cultrone, A., Lammens, T., Boudolf, V., Brown, S.C., Kondorosi, E., Apc/c-ccs52a complexes control meristem maintenance in the arabidopsis root (2009) Proc. Natl. Acad. Sci. U S A, 106 (28), pp. 11806-11811; 
Beemster, G., Baskin, T., Analysis of cell division and elongation underlying the developmental acceleration of root growth in arabidopsis thaliana (1998) Plant Physiology, 116, pp. 1515-1526; 
Iwamoto, A., Satoh, D., Furutani, M., Maruyama, S., Ohba, H., Sugiyama, M., Insight into the basis of root growth in arabidopsis thaliana provided by a simple mathematical model (2006) J. Plant Res., 119, pp. 85-93; 
Immink, R., Gadella, T., Ferrario, S., Busscher, M., Angenent, G., Analysis of mads box protein-protein interactions in living plant cells (2002) Proc. Natl. Acad. Sci. USA, 99, pp. 2416-2421; 
Willemse, J., Kulikova, O., Jong, H., Bisseling, T., A new whole-mount dna quantification method and the analysis of nuclear dna content in the stem-cell niche of arabidopsis roots (2008) The Plant Journal, 10, pp. 1365-1374; 
Marcuzzo, M., Quelhas, P., Campilho, A., Mendon{\c c}a, A.M., Campilho, A., Automated arabidopsis plant root cell segmentation based on svm classification and region merging (2009) Comp. in Biology and Medicine, 39, pp. 785-793; 
Marcuzzo, M., Guichard, T., Quelhas, P., Mendon{\c c}a, A.M., Campilho, A., Cell division detection on the arabidopsis thaliana root (2009) LNCS, 5524, pp. 168-175. , Araujo, H., Mendon{\c c}a, A.M., Pinho, A.J., Torres, M.I. (eds.) IbPRIA 2009. Springer, Heidelberg; 
Kovesi, P., Symmetry and asymmetry from local phase Proc. of the 10th Australian Joint Conf. on Artificial Intelligence (December 1997); 
Campilho, A., Garcia, B., Toorn, H., Wijk, H., Campilho, A., Scheres, B., Time-lapse analysis of stem-cell divisions in the arabidopsis thaliana root meristem (2006) The Plant Journal, 48, pp. 619-627; 
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K., Image denoising by sparse 3d transform-domain collaborative filtering (2007) IEEE Trans. on Image Processing, 16 (8), pp. 2080-2095; 
Otsu, N., A threshold selection method from gray-level histograms (1979) IEEE Transactions on Systems, Man, and Cybernetics, SMC-9 (1), pp. 62-66
},
	month = {2011///},
	pages = {1 - 11},
	address = {Burnaby, BC},
	abstract = {In plant development biology, the study of the structure of the plant{\textquoteright}s root is fundamental for the understanding of the regulation and interrelationships of cell division and cellular differentiation. This is based on the high connection between cell length and progression of cell differentiation and the nuclear state. However, the need to analyse a large amount of images from many replicate roots to obtain reliable measurements motivates the development of automatic tools for root structure analysis. We present a novel automatic approach to detect cell files, the main structure in plant roots, and extract the length of the cells in those files. This approach is based on the detection of local cell file characteristic symmetry using a wavelet based image symmetry measure. The resulting detection enables the automatic extraction of important data on the plant development stage and of characteristics for individual cells. Furthermore, the approach presented reduces in more than 90\% the time required for the analysis of each root, improving the work of the biologist and allowing the increase of the amount of data to be analysed for each experimental condition. While our approach is fully automatic a user verification and editing stage is provided so that any existing errors may be corrected. Given five test images it was observed that user did not correct more than 20\% of all automatically detected structure, while taking no more than 10\% of manual analysis time to do so. {\textcopyright} 2011 Springer-Verlag.},
	keywords = {Arabidopsis thaliana, Automatic extraction, Automatic tools, Bio-imaging, BioImaging, cell differentiation, Cell divisions, Cell lengths, cell proliferation, Cells, Cellular differentiation, Experimental conditions, Image analysis, image symmetry, Individual cells, Main structure, Manual analysis, Nuclear state, Plant development, Plant roots, Plants (botany), Reliable measurement, Root structure, Test images, User verification, Wavelet analysis, Wavelet-based images},
	isbn = {03029743 (ISSN); 9783642215957 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960337749\&partnerID=40\&md5=ef49700082788511730e38d4eda9a566},
	author = {Quelhas, P. and Nieuwland, J. and Dewitte, W. and Mendon{\c c}a, A.M. and Murray, J. and Campilho, A.}
}
@book {2221,
	title = {Classification-based segmentation of the region of interest in chromatographic images},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {6754 LNCS},
	year = {2011},
	note = {Conference code: 85546Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-21596-4_8Language of Original Document: EnglishCorrespondence Address: Sousa, A.V.; Instituto de Engenharia Biom{\'e}dica, Universidade Do Porto, Rua Roberto Frias, s/n, 4200-465 Porto, Portugal; email: asousa@eu.ipp.ptReferences: Zarate, Y., Hopkin, R., Fabry{\textquoteright}s Disease (2008) The Lancet, 372 (9647), pp. 1427-1435;Linthorst, G.E., Vedder, A., Aerts, J.M., Hollak, C.E., Screening for Fabry Disease Using Whole Blood Spots Fails to Identify One-third of Female Carriers (2005) Clinica Chimica Acta, 353 (1-2), pp. 201-203; 
Eng, C.M., Germain, D.P., Banikazemi, M., Warnock, D.G., Wanner, C., Hopkin, R.J., Bultas, J., Wilcox, W.R., Fabry Disease: Guidelines for the Evaluation and Management of Multi-organ System Involvement (2006) Genetics in Medicine, 8 (9), pp. 539-548; 
Gerasimov, A.V., Use of the Software Processing of Scanned Chromatogram Images in Quantitative Planar Chromatography (2004) J. of Anal. Chem., 59 (4), pp. 348-353; 
Bajla, I., Hollander, I., Fluch, S., Burg, K., Kollar, M., An Alternative Method for Electrophoretic Gel Image Analysis in the Gelmaster Software (2005) Comput. Methods Programs Biomed., 77 (3), pp. 209-231; 
Sousa, A.V., Mendon{\c c}a, A.M., Campilho, A., Chromatographic Pattern Classification (2008) IEEE Trans. on Biomedical Engineering, 55 (6), pp. 1687-1696; 
Bajla, I., Rubl{\'\i}k, F., Arendack{\'a}, B., Farka{\v s}, I., Horni{\v s}ov{\'a}, K., {\v S}tolc, S., Witkovsk{\'y}, V., Segmentation and Supervised Classification of Image Objects in Epo Doping-control (2009) Machine Vision and Applications, 20 (4), pp. 243-259; 
Sousa, A.V., Aguiar, R., Mendon{\c c}a, A.M., Campilho, A., Automatic Lane and Band Detection in Images of Thin Layer Chromatography (2004) LNCS, 3212, pp. 158-165. , Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. Springer, Heidelberg; 
Mendon{\c c}a, A.M., Sousa, A.V., S{\'a}-Miranda, M.C., Campilho, A., Automatic segmentation of chromatographic images for region of interest delineation (2011) SPIE Medical Imaging; 
Dempster, A., Laird, N., Rubin, D., Maximum Likelihood from Incomplete Data via the EM Algorithm (1997) J. of the Royal Statistical Society. Series B, 39 (1), pp. 1-38; 
Heijden, F., Robert, P.W.D., Ridder, D., Tax, D.M.J., (2004) Classification, Parameter Estimation and State Estimation, , John Wiley \& Sons, Chichester
},
	month = {2011///},
	pages = {68 - 78},
	address = {Burnaby, BC},
	abstract = {This paper proposes a classification-based method for automating the segmentation of the region of interest (ROI) in digital images of chromatographic plates. Image segmentation is performed in two phases. In the first phase an unsupervised learning method classifies the image pixels into three classes: frame, ROI or unknown. In the second phase, distance features calculated for the members of the three classes are used for deciding on the new label, ROI or frame, for each individual connected segment previously classified as unknown.The segmentation result is post-processed using a sequence of morphological operators before obtaining the final ROI rectangular area. The proposed methodology, which is the initial step for the development of a screening tool for Fabry disease, was successfully evaluated in a dataset of 58 chromatographic images. {\textcopyright} 2011 Springer-Verlag.},
	keywords = {Chromatographic images, Chromatographic plates, Chromatography, Data sets, Digital image, Distance feature, Fabry disease, Image analysis, Image pixels, Image segmentation, Morphological operator, Rectangular area, Region of interest, Region of interest delineation, Screening tool, Second phase, Segmentation results, Unsupervised learning, Unsupervised learning method},
	isbn = {03029743 (ISSN); 9783642215957 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960331409\&partnerID=40\&md5=c248f56c8a7bda71832c4b569e32164b},
	author = {Sousa, A.V. and Mendon{\c c}a, A.M. and S{\'a}-Miranda, M.C. and Campilho, A.}
}
@conference {2219,
	title = {Low frame rate cell tracking: A Delaunay graph matching approach},
	booktitle = {Proceedings - International Symposium on Biomedical ImagingIEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn.},
	series = {2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI{\textquoteright}11},
	year = {2011},
	note = {Conference code: 87101Export Date: 21 November 2012Source: ScopusArt. No.: 5872573doi: 10.1109/ISBI.2011.5872573Language of Original Document: EnglishCorrespondence Address: Chen, Y.; INEB - Instituto de Engenharia Biom{\'e}dica, Rua do Campo Alegre, 823, 4150-180 Porto, PortugalReferences: Godinez, W.J., Lampe, M., Worz, S., Muller, B., Eils, R., Rohr, K., Tracking of virus particles in time-lapse fluorescence microscopy image sequences (2007) Proceedings of the IEEE International Symposium on Biomedical Imaging, pp. 256-259;Finch, A.W., Wilson, R.C., Hancock, E.R., Symbolic graph matching with the em algorithm (1998) Pattern Recognit, 31 (11), pp. 1777-1790. , Nov; 
Finch, M.A., Wilson, C.R., Hancock, R.E., Matching delaunay graphs (1997) Pattern Recognit, 30 (1), pp. 123-140. , Nov; 
Quelhas, P., Marcuzzo, M., Mendon{\c c}a, A.M., Campilho, A., Cell nuclei and cytoplasm joint segmentation using the sliding band filter (2010) IEEE Transactions on Medical Imaging, 29, pp. 1463-1473; 
Fortune, S., A sweepline algorithm for voronoi diagram (1987) Algorithmica, 2, pp. 153-174; 
Khoo, K., Suganthan, P., Evaluation of genetic operators and solution representations for shape recognition by genetic algorithms (2002) Patt. Recognit. Lett., 23 (13), pp. 1589-1597; 
Caetano, T.S., Caelli, T., Schuurmans, D., Barone, D., Graphical models and point pattern matching (2006) IEEE Trans. on Pattern Analysis and Machine Intelligence, 28 (10), pp. 1646-1662. , Oct; 
Raveaux, R., Burie, J.-C., Ogier, J.-M., A graph matching method and a graph matching distance based on subgraph assignments (2010) Pattern Recognition Letters, 31, pp. 394-406; 
Marcuzzo, M., Quelhas, P., Campilho, A., Cell division detection on the arabidopsis thaliana root (2009) IbPRIA 2009, 5524, pp. 168-175. , LNCS
Sponsors: Inst. Electr. Electron. Eng., Eng. Med. Biol. Soc. (IEEE EMBS); Inst. Electr. Electron. Eng., Signal Process. Soc. (IEEE SPS)},
	month = {2011///},
	pages = {1015 - 1018},
	address = {Chicago, IL},
	abstract = {Cell tracking is a fundamental problem for studying live cell dynamics. A novel approach for low frame rate cell tracking in fluorescent microscopy image is herein proposed. The method is based on sliding band filter detection and Delaunay triangulation sub-graph matching. With this approach we can track a large amount of small cells without their motion model and not relying on cell motion continuity between consecutive images. The effectiveness and robustness of the method were validated by visual inspection and on a ground truth dataset. {\textcopyright} 2011 IEEE.},
	keywords = {Band filters, cell motion, Cell tracking, Cells, Consecutive images, Data sets, Delaunay graph, Delaunay graph matching, Delaunay triangulation, Fluorescent microscopy, Frame rate, Fundamental problem, Ground truth, Live cell, low frame rate tracking, Medical imaging, Motion models, small target tracking, Small targets, Target tracking, Visual inspection},
	isbn = {19457928 (ISSN); 9781424441280 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-80055027991\&partnerID=40\&md5=f347799ea22097a5550939fa287cd4ab},
	author = {Chen, Y. and Quelhas, P. and Campilho, A.}
}
@article {2242,
	title = {Segmentation of ultrasound images of the carotid using RANSAC and cubic splines},
	journal = {Computer Methods and Programs in BiomedicineComput. Methods Programs Biomed.},
	volume = {101},
	year = {2011},
	note = {Cited By (since 1996): 10Export Date: 21 November 2012Source: ScopusCODEN: CMPBEdoi: 10.1016/j.cmpb.2010.04.015PubMed ID: 20554343Language of Original Document: EnglishCorrespondence Address: Rocha, R.; INEB, Divis{\~a}o de Sinal e Imagem, Campus FEUP, 4200-465 Porto, Portugal; email: rhr@isep.ipp.ptReferences: Baldassarre, D., Amato, M., Bondioli, A., Sirtori, C.R., Tremoli, E., Carotid artery intima-media thickness measured by ultrasonography in normal clinical practice correlates well with atherosclerosis risk factors (2000) Stroke, 31, pp. 2426-2430;O{\textquoteright}Leary, D., Polak, J., Kronmal, R., Manolio, T., Burke, G.W.S., Carotid-artery intima and media thickness as a risk factor for myocardial infarction and stroke in older adults (1999) N. Engl. J. Med., 340, pp. 14-22; 
Hodis, H., Mack, W., LaBree, L., Selzer, R., Liu, C., Liu, C., Azen, S., The role of carotid arterial intima-media thickness in predicting clinical coronary events (1998) Ann. Intern. Med., 128 (4), pp. 262-269; 
Reneman, R.S., Meinders, J.M., Hoeks, A.P.G., Non-invasive ultrasound in arterial wall dynamics in humans: what have we learned and what remains to be solved (2005) Eur. Heart J., 26, pp. 960-966; 
Gee, A., Prager, R., Treece, G., Berman, L., Engineering a freehand 3D ultrasound system (2003) Pattern Recogn. Lett., 24 (4-5), pp. 757-777; 
Sonka, M., Liang, W., Stefancik, R., Stolpen, A., (2000), 2. , Handbook of Medical Imaging: Medical Image Processing and Analysis,SPIEGiannoglou, G.D., Chatzizisis, Y.S., Koutkias, V., Kompatsiaris, I., Papadogiorgaki, M., Mezaris, V., Parissi, E., Louridas, G.E., A novel active contour model for fully automated segmentation of intravascular ultrasound images: in vivo validation in human coronary arteries (2008) Comput. Biol. Med., 37 (9), pp. 1292-1302; 
Tsai, A., Yezzi, A.J., Tempany, C., Tucker, D., Fan, A., Grimson, W.E.L., Willsky, A.S., A shape-based approach to the segmentation of medical imagery using level sets (2003) IEEE Trans. Med. Imaging, 22 (2), pp. 137-154; 
Cremers, D., Kohlberger, T., Schnorr, C., Nonlinear shape statistics in Mumford-Shah based segmentation (2002) Proc. European Conf. on Computer Vision (ECCV{\textquoteright}2002), vol. 2351, pp. 93-108; 
Cremers, D., Sochen, N., Schnorr, C., Towards recognition-based variational segmentation using shape priors and dynamic labeling (2003) Proc. European Conf. on Computer Vision (ECCV{\textquoteright}2003), vol. 2695, pp. 388-400; 
Cremers, D., Osher, S., Soatto, S., Kernel density estimation and intrinsic alignment for knowledge-driven segmentation: teaching level sets to walk (2004) Pattern Recognit., 3175, pp. 36-44; 
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Tauber, C., (2005), Filtrage anisotrope robuste et segmentation par b-spline snake: application aux images {\'e}chographiques, Ph.D. thesis, Institut National Polytechnique de Toulouse, ToulouseYu, Y., Acton, S., Speckle reducing anisotropic diffusion (2002) IEEE Trans. Image Process., 11 (11), pp. 1260-1270; 
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},
	month = {2011///},
	pages = {94 - 106},
	abstract = {A new algorithm is proposed for the semi-automatic segmentation of the near-end and the far-end adventitia boundary of the common carotid artery in ultrasound images. It uses the random sample consensus method to estimate the most significant cubic splines fitting the edge map of a longitudinal section. The consensus of the geometric model (a spline) is evaluated through a new gain function, which integrates the responses to different discriminating features of the carotid boundary: the proximity of the geometric model to any edge or to valley shaped edges; the consistency between the orientation of the normal to the geometric model and the intensity gradient; and the distance to a rough estimate of the lumen boundary.A set of 50 longitudinal B-mode images of the common carotid and their manual segmentations performed by two medical experts were used to assess the performance of the method. The image set was taken from 25 different subjects, most of them having plaques of different classes (class II to class IV), sizes and shapes.The quantitative evaluation showed promising results, having detection errors similar to the ones observed in manual segmentations for 95\% of the far-end boundaries and 73\% of the near-end boundaries. {\textcopyright} 2011 Elsevier Ireland Ltd.},
	keywords = {Algorithms, article, B scan, B-mode images, Carotid, carotid artery, Carotid Artery Diseases, Carotid Artery, Common, Class II, clinical article, Common carotid artery, Cubic spline, Detection error, echography, Edge map, Female, Gain function, Geometric models, Geometry, human, Humans, Image Interpretation, Computer-Assisted, image processing, Image segmentation, Image sets, Intensity gradients, Longitudinal section, male, Manual segmentation, mathematical model, Medical experts, Medical imaging, Non-linear smoothing, Pattern Recognition, Automated, quantitative analysis, Quantitative evaluation, random sample, Random sample consensus, RANSAC, Semi-automatic segmentation, Splines, Ultrasonic applications, Ultrasonics, Ultrasonography, Ultrasound image, Ultrasound images},
	isbn = {01692607 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-78650679026\&partnerID=40\&md5=8bc951c3b232b4b7dd95eea0fc8671ef},
	author = {Rocha, R. and Campilho, A. and Silva, J. and Azevedo, E. and Santos, R.}
}
@article {2267,
	title = {Cell nuclei and cytoplasm joint segmentation using the sliding band filter},
	journal = {IEEE Transactions on Medical ImagingIEEE Trans. Med. Imaging},
	volume = {29},
	year = {2010},
	note = {Cited By (since 1996): 6Export Date: 21 November 2012Source: ScopusArt. No.: 5477157CODEN: ITMIDdoi: 10.1109/TMI.2010.2048253Language of Original Document: EnglishCorrespondence Address: Quelhas, P.; Instituto de Engenharia Biom{\'e}dica (INEB), 4200-465 Porto, Portugal; email: pedro.quelhas@gmail.comChemicals/CAS: DNA, 9007-49-2; Actins; DNA, 9007-49-2References: Bleau, A., Leon, L., Watershed-based segmentation and region merging (2000) Comput. Vis. Image Understand., 77 (3), pp. 317-370;Clocksin, W., Automatic segmentation of overlapping nuclei with high background variation using robust estimation and flexible contour models (2003) Proc. 12th Int. Conf. Image Anal. Process., pp. 682-687; 
Cloppet, F., Boucher, A., Segmentation of overlapping/aggregating nuclei cells in biological images (2008) Proc. 19th Int. Conf. Pattern Recognit. (ICPR 2008), pp. 1-4; 
Danek, O., Matula, P., De Solorzano, C.O., Munoz-Barrutia, A., Maska, M., Kozubek, M., Segmentation of touching cell nuclei using a two-stage graph cut model (2009) LNCS, 5575, pp. 410-419. , Proc. SCIA; 
Fok, Y., Chan, J., Chin, R., Automated analysis of nerve-cell images using active contour models (1996) IEEE Trans. Med. Imag., 15 (3), pp. 353-368. , Jun; 
Harder, N., M-Bermudez, F., Godinez, W., Ellenberg, J., Eils, R., Rohr, K., Automated analysis of mitotic cell nuclei in 3-D fluorescence microscopy image sequences (2008) Workshop Bio-Image Inf.: Biol. Imag., Comput. Vis. Data Mining, pp. 17-18. , Santa Barbara, CA; 
Hill, A., Lapan, P., Li, Y., Haney, S., Impact of image segmentation on high-content screening data quality for sk-br-3 cells (2007) BMC Bioin-formatics, 8 (340), pp. 1-13; 
Hu, M., Ping, X., Ding, Y., A new active contour model and its application on cell segmentation (2004) 2004 8th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2, pp. 1104-1107. , TuA7.09 - P0673, 2004 8th International Conference on Control, Automation, Robotics and Vision (ICARCV); 
Jones, T.R., Carpenter, A., Golland, P., Voronoi-based segmentation of cells on image manifolds (2005) ICCV Workshop Comput. Vis. Biomed. Image Appl. (CVBIA), pp. 535-543; 
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Marcuzzo, M., Guichard, T., Quelhas, P., Mendon{\c c}a, A.M., Campilho, A., Cell division detection on the arabidopsis thaliana root (2009) LNCS, 5524, pp. 168-175. , Proceedings of IbPRIA. New York: Springer; 
Marcuzzo, M., Quelhas, P., Mendon{\c c}a, A.M., Campilho, A., Evaluation of symmetry enhanced sliding band filter for plant cell nuclei detection in low contrast noisy fluorescent images (2009) LNCS, 5627, pp. 824-831. , Proceedings of ICIAR. New York: Springer; 
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Pereira, C.S., Mendon{\c c}a, A.M., Campilho, A., Evaluation of contrast enhancement filters for lung nodule detection Proceedings of ICIAR. New York: Springer, 4633, pp. 878-888; 
Quelhas, P., Marcuzzo, M., Oliveira, M.J., Mendon{\c c}a, A.M., Campilho, A., Cancer cell detection and invasion depth estimation in brightfield images (2009) Proc. 20th Br. Mach. Vis. Conf. (BMVC), , London, U.K; 
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Xiong, G., Zhou, X., Ji, L., Automated segmentation of drosophila RNAi fluorescence cellular images using deformable models (2006) IEEE Trans. Circ. Syst. i, 53 (11), pp. 2415-2424. , Nov; 
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},
	month = {2010///},
	pages = {1463 - 1473},
	abstract = {Microscopy cell image analysis is a fundamental tool for biological research. In particular, multivariate fluorescence microscopy is used to observe different aspects of cells in cultures. It is still common practice to perform analysis tasks by visual inspection of individual cells which is time consuming, exhausting and prone to induce subjective bias. This makes automatic cell image analysis essential for large scale, objective studies of cell cultures. Traditionally the task of automatic cell analysis is approached through the use of image segmentation methods for extraction of cells{\textquoteright} locations and shapes. Image segmentation, although fundamental, is neither an easy task in computer vision nor is it robust to image quality changes. This makes image segmentation for cell detection semi-automated requiring frequent tuning of parameters. We introduce a new approach for cell detection and shape estimation in multivariate images based on the sliding band filter (SBF). This filter{\textquoteright}s design makes it adequate to detect overall convex shapes and as such it performs well for cell detection. Furthermore, the parameters involved are intuitive as they are directly related to the expected cell size. Using the SBF filter we detect cells{\textquoteright} nucleus and cytoplasm location and shapes. Based on the assumption that each cell has the same approximate shape center in both nuclei and cytoplasm fluorescence channels, we guide cytoplasm shape estimation by the nuclear detections improving performance and reducing errors. Then we validate cell detection by gathering evidence from nuclei and cytoplasm channels. Additionally, we include overlap correction and shape regularization steps which further improve the estimated cell shapes. The approach is evaluated using two datasets with different types of data: a 20 images benchmark set of simulated cell culture images, containing 1000 simulated cells; a 16 images Drosophila melanogaster Kc167 dataset containing 1255 cells, stained for DNA and actin. Both image datasets present a difficult problem due to the high variability of cell shapes and frequent cluster overlap between cells. On the Drosophila dataset our approach achieved a precision/recall of 95\%/69\% and 82\%/90\% for nuclei and cytoplasm detection respectively and an overall accuracy of 76\%. {\textcopyright} 2010 IEEE.},
	keywords = {actin, Actins, animal, Animals, article, Band filters, Biological research, cell aggregation, Cell analysis, Cell culture, Cell detection, Cell images, cell nucleus, Cell segmentation, Cell Shape, Cell shapes, Cell size, Cells, chemistry, Computer vision, Convergence filters, Convex shapes, cytoplasm, Data sets, Databases, Factual, Digital image storage, DNA, Drosophila melanogaster, factual database, Fluorescence, Fluorescence microscopy, Fundamental tools, High variability, Image analysis, Image datasets, image processing, image processing in biology, Image Processing, Computer-Assisted, Image quality, Image segmentation, Imaging systems, Improving performance, Individual cells, Joint segmentation, Location, methodology, Microscopy, Fluorescence, Multivariate analysis, multivariate image processing, New approaches, Nuclear detection, Overlap correction, reproducibility, Reproducibility of Results, Semi-automated, Shape estimation, Shape regularization, ultrastructure, Visual communication, Visual inspection},
	isbn = {02780062 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77955339493\&partnerID=40\&md5=f8cea406f7b2fcf901dc34fa940522b1},
	author = {Quelhas, P. and Marcuzzo, M. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@book {2262,
	title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {6112 LNCS},
	year = {2010},
	note = {Conference code: 81097Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Campilho, A.; University of Porto, Faculty of Engineering, Institute of Biomedical Engineering, 4200-465 Porto, Portugal; email: campilho@fe.up.pt},
	month = {2010///},
	pages = {V - VI},
	address = {Povoa de Varzim},
	isbn = {03029743 (ISSN); 3642137741 (ISBN); 9783642137747 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77955371702\&partnerID=40\&md5=39645611f42b004904f05cef3083d93d},
	author = {Campilho, A. and Kamel, M.}
}
@book {2263,
	title = {Optical flow based Arabidopsis thaliana root meristem cell division detection},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {6112 LNCS},
	year = {2010},
	note = {Conference code: 81097Cited By (since 1996): 1Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-13775-4_22Language of Original Document: EnglishCorrespondence Address: Quelhas, P.; INEB - Instituto de Engenharia Biom{\'e}dica, Divis{\~a}o de Sinal e Imagem, Campus FEUPPortugalReferences: Campilho, A., Garcia, B., Toorn, H.V.D., Wijk, H.V., Campilho, A., Scheres, B., Time-lapse analysis of stem-cell divisions in the Arabidopsis thaliana root meristem (2006) Plant Journal, 48 (4), pp. 619-627. , DOI 10.1111/j.1365-313X.2006.02892.x;Iwamoto, A., Satoh, D., Furutani, M., Maruyama, S., Ohba, H., Sugiyama, M., Insight into the basis of root growth in arabidopsis thaliana provided by a simple mathematical model (2006) J. Plant Res., 119, pp. 85-93; 
Roberts, T., Mckenna, S., Wuyts, N., Valentine, T., Bengough, A., Performance of low-level motion estimation methods for confocal microscopy of plant cells in vivo (2007) Motion{\textquoteright}07, p. 13; 
Beemster, G.T.S., Baskin, T.I., Analysis of Cell Division and Elongation Underlying the Developmental Acceleration of Root Growth in Arabidopsis thaliana (1998) Plant Physiology, 116 (4), pp. 1515-1526; 
Chen, X., Zhou, X., Wong, S.T.C., Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy (2006) IEEE Trans. on Biomedical Engineering, 53 (4), pp. 762-766; 
Mao, K.Z., Zhao, P., Tan, P., Supervised learning-based cell image segmentation for p53 immunohistochemistry (2006) IEEE Trans. on Biomedical Engineering, 53 (6), pp. 1153-1163; 
Harder, N., M-Bermudez, F., Godinez, W., Ellenberg, J., Rohr, K., Automated analysis of mitotic cell nuclei in 3rd fluorescence microscopy image sequences Workshop on Bio-Image Inf.: Bio. Imag., Comp. Vision and Data Mining (2008); 
Yang, F., Mackey, M.A., Ianzini, F., Gallardo, G., Sonka, M., Cell segmentation, tracking, and mitosis detection using temporal context (2005) Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 302-309; 
Marcuzzo, M., Quelhas, P., Campilho, A., Mendon{\c c}a, A.M., Campilho, A., Automated arabidopsis plant root cell segmentation based on svm classification and region merging (2009) Comp. in Biology and Medicine, 39, pp. 785-793; 
Marcuzzo, M., Guichard, T., Quelhas, P., Maria Mendon{\c c}a, A., Campilho, A., Cell division detection on the arabidopsis thaliana root (2009) LNCS, 5524, pp. 168-175. , Araujo, H., Mendon{\c c}a, A.M., Pinho, A.J., Torres, M.I. (eds.) IbPRIA 2009. Springer, Heidelberg; 
Pereira, C.S., Mendon{\c c}a, A.M., Campilho, A., Evaluation of contrast enhancement filters for lung nodule detection (2007) LNCS, 4633, pp. 878-888. , Kamel, M.S., Campilho, A. (eds.) ICIAR 2007. Springer, Heidelberg; 
Garcia, B., Campilho, A., Scheres, B., Campilho, A., Automatic tracking of arabidopsis thaliana root meristem in confocal microscopy (2004) LNCS, 3212, pp. 166-174. , Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. Springer, Heidelberg; 
Hartley, R., Zisserman, A., (2003) Multiple View Geometry in Computer Vision, , Cambridge University Press, Cambridge; 
Lowe, D.G., Distinctive image features from scale-invariant keypoints (2004) International Journal of Computer Vision, 60, pp. 91-110; 
Brox, T., Bruhn, A., Papenberg, N., Weickert, J., High accuracy optical flow estimation based on a theory for warping (2004) LNCS, 3024, pp. 25-36. , Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. Springer, Heidelberg
},
	month = {2010///},
	pages = {217 - 226},
	address = {Povoa de Varzim},
	abstract = {The study of cell division and growth is a fundamental aspect of plant biology research. In this research the Arabidopsis thaliana plant is the most widely studied model plant and research is based on in vivo observation of plant cell development, by time-lapse confocal microscopy. The research herein described is based on a large amount of image data, which must be analyzed to determine meaningful transformation of the cells in the plants. Most approaches for cell division detection are based on the morphological analysis of the cells{\textquoteright} segmentation. However, cells are difficult to segment due to bad image quality in the in vivo images. We describe an approach to automatically search for cell division in the Arabidopsis thaliana root meristem using image registration and optical flow. This approach is based on the difference of speeds of the cell division and growth processes (cell division being a much faster process). With this approach, we can achieve a detection accuracy of 96.4\%. {\textcopyright} 2010 Springer-Verlag.},
	keywords = {Arabidopsis thaliana, Biology, Biology image processing, cell division detection, Cell divisions, cell proliferation, confocal microscopy, Detection accuracy, Growth process, Image data, Image quality, Image segmentation, Imaging systems, In-vivo, In-vivo images, Metadata, Morphological analysis, Optical data processing, Optical flows, Plant biology, Plant cells, research, Root meristem},
	isbn = {03029743 (ISSN); 3642137741 (ISBN); 9783642137747 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77955346994\&partnerID=40\&md5=22ef87a91388dfe96acf9e4ac6ad4377},
	author = {Quelhas, P. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@article {2282,
	title = {Segmentation of the carotid intima-media region in B-mode ultrasound images},
	journal = {Image and Vision ComputingImage Vision Comput},
	volume = {28},
	year = {2010},
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Kern, R., Szabo, K., Hennerici, M., Meairs, S., Characterization of carotid artery plaques using real-time compound B-mode ultrasound (2004) Stroke, 35, pp. 870-875; 
Ellis, S.M., Naoumova, R.P., Neuwirth, C.K., Eckersley, R., Cosgrove, D.O., Thompson, G.R., Sidhu, P.S., Measurement of the reflectivity of the intima-medial layer of the common carotid artery improves the discriminatory value of intima-medial thickness measurement as a predictor of risk of atherosclerotic disease (2007) Ultrasound Med. Biol., 33 (7), pp. 1029-1038; 
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Rocha, R., (2007) Image Segmentation and Reconstruction of 3D Surfaces from Carotid Ultrasound Images, , http://www.ineb.fe.up.pt/bmivic, Ph.D. thesis, Faculdade de Engenharia da Universidade do Porto, Porto July; 
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Selzer, R.H., Hodis, H.N., Kwong-Fu, H., Mack, W.J., Lee, P.L., Liu, C.R., Liu, C.H., Evaluation of computerized edge tracking for quantifying intima-media thickness of the common carotid artery from B-mode ultrasound images (1994) Atherosclerosis, 111, pp. 1-11; 
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Automatic detection of the intimal and the adventitial layers of the common carotid artery wall in ultrasound Bmode images using snakes, in: International Conference on Image Analysis and Processing, B. Werer, 1999, pp. 452-457Liang, Q., Wendelhag, I., Wikstrand, J., Gustavsson, T., A multiscale dynamic programming procedure for boundary detection in ultrasound artery images (2000) IEEE Trans. Med. Imag., 19 (2), pp. 127-142; 
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Schmidt-Trucksass, A., Cheng, D., Sandrock, M., Schulte-Monting, J., Rauramaa, R., Huonker, M., Burkhardt, H., Computerized analysing system using the active contour in ultrasound measurement of carotid artery intima-media thickness (2001) Clin. Physiol., 5, pp. 561-569; 
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Rudin, L., Osher, S., Total variation based image restoration with free local constraints (1994) Proceedings of IEEE International Conference on Image Processing (ICIP{\textquoteright}94), 1, pp. 31-35; 
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Breu, H., Gil, J., Kirkpatrick, D., Werman, M., Linear time Euclidean distance transform algorithms (1995) IEEE Trans. Pattern Anal. Mach. Intell., 17 (5), pp. 529-533; 
Bland, J.M., Altman, D.G., Statistical methods for assessing agreement between two methods of clinical measurement (1986) Lancet, 1 (8476), pp. 307-310; 
Loizou, C.P., Pattichis, C.S., Pantziaris, M., Nicolaides, A., An integrated system for the segmentation of atherosclerotic carotid plaque (2007) IEEE Trans. Info. Tech. Biomed., 11 (6), pp. 661-667; 
de Boor, C., (1978) A Practical Guide to Splines, , Springer-Verlag; 
Unser, M., Splines: a perfect fit for signal and image processing (1999) IEEE Signal Processing Magazine, 16 (6), pp. 22-38; 
Yang, Z., Tuthill, T.A., Raunig, D.L., Fox, M.D., Analoui, M., Pixel compounding: resolution-enhanced ultrasound imaging for quantitative analysis (2007) Ultrasound Med. Biol., 33 (8), pp. 1309-1319
},
	month = {2010///},
	pages = {614 - 625},
	abstract = {This paper proposes a new approach for the segmentation of both near-end and far-end intima-media regions of the common carotid artery in ultrasound images. The method requires minimal user interaction and is able to segment the near-end wall in arteries with large, hypoechogenic and irregular plaques, issues usually not considered previously due to the increased segmentation difficulty. The adventitia is detected by searching for the best fit of a cubic spline to edges having features compatible with the adventitia boundary. The algorithm uses a global smoothness constraint and integrates discriminating features of the adventitia to reduce the attraction by other edges. Afterwards, using the information of the adventitia location, the lumen boundary is detected by combining dynamic programming, smooth intensity thresholding surfaces and geometric snakes. Smooth contours that correctly adapt to the intima are produced, even in the presence of deep concavities. Moreover, unlike balloon-based snakes, the propagation force does not depend on gradients and does not require a predefined direction. An extensive statistical evaluation is computed, using a set of 47 images from 24 different symptomatic patients, including several classes, sizes and shapes of plaques. Bland-Altman plots of the mean intima-media thickness, for manual segmentations of two medical experts, show a high intra-observer and inter-observer agreement, with mean differences close to zero (mean between -0.10 mm and 0.18 mm) and with the large majority of differences within the limits of agreement (standard deviation between 0.10 mm and 0.12 mm). Similar plots reveal a good agreement between the automatic and the manual segmentations (mean between -0.07 mm and 0.11 mm and standard deviation between 0.11 mm and 0.12 mm). {\textcopyright} 2009 Elsevier B.V. All rights reserved.},
	keywords = {Acoustic waves, B-mode ultrasound images, Best fit, Carotid, Common carotid artery, Cubic spline, Digital image storage, Dynamic programming, Edge detection, End-wall, Function evaluation, Geometric snake, Geometric snakes, Image segmentation, Intima-media thickness, Manual segmentation, Medical experts, New approaches, Smoothness constraints, Standard deviation, Statistical evaluation, statistics, Thresholding, Ultrasonics, Ultrasound image, Ultrasound images, User interaction},
	isbn = {02628856 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-74749094600\&partnerID=40\&md5=bf1945408da50912250acf34d647acc8},
	author = {Rocha, R. and Campilho, A. and Silva, J. and Azevedo, E. and Santos, R.}
}
@article {2300,
	title = {Automated Arabidopsis plant root cell segmentation based on SVM classification and region merging},
	journal = {Computers in Biology and MedicineComput. Biol. Med.},
	volume = {39},
	year = {2009},
	note = {Cited By (since 1996): 9Export Date: 21 November 2012Source: ScopusCODEN: CBMDAdoi: 10.1016/j.compbiomed.2009.06.008PubMed ID: 19604506Language of Original Document: EnglishCorrespondence Address: Marcuzzo, M.; INEB - Instituto de Engenharia Biom{\'e}dica, Divis{\~a}o de Sinal e Imagem, Campus FEUP, Portugal; email: marcuzzo@fe.up.ptReferences: Baeza-Yates, R., Ribeiro-Neto, B., (1999) Modern Information Retrieval, , ACM Press;Bleau, A., Leon, L., Watershed-based segmentation and region merging (2000) Computer Vision and Image Understanding, 77 (3), pp. 317-370; 
M. Brown, W. Grundy, L.D.N. Cristianini, C. Sugnet, T. Furey, M. Ares, D. Haussler, Knowledge-based analysis of microarray gene expression data using support vector machines, in: Proceedings of the National Academy of Sciences, 97 (1), 1999, pp. 262-267Burges, C., A tutorial on support vector machines for pattern recognition (1998) Data Mining and Knowledge Discovery, 2 (2), pp. 121-167; 
Campilho, A., Garcia, B., Toorn, H., Wijk, H., Campilho, A., Scheres, B., Time-lapse analysis of stem-cell divisions in the Arabidopsis thaliana root meristem (2006) Plant Journal, 48, pp. 619-627; 
Chang, C., Lin, C., (2001) Libsvm: A library for support vector machines, , http://www.csie.ntu.edu.tw/\~{}cjlin/libsvm/faq.html$\#$f203, Software available online at; 
Chen, X., Zhou, X., Wong, S.T.C., Automated segmentation classification, and tracking of cancer cell nuclei in time-lapse microscopy (2006) IEEE Transactions on Biomedical Engineering, 53 (4), pp. 762-766; 
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K., Image denoising by sparse 3d transform-domain collaborative filtering (2007) IEEE Transactions on Image Processing, 16 (8), pp. 2080-2095; 
(2004) Kernel Methods in Computational Biology, Computational Molecular Biology, , B. Sch{\"o}lkopf, K. Tsuda, J.-P. Vert, Eds, MIT Press, Cambridge, MA; 
Garcia, B., Campilho, A., Scheres, B., Campilho, A., Automatic tracking of Arabidopsis thaliana root meristem in confocal microscopy (2004) Springer Lecture Notes in Computer Science, 3212, pp. 166-174. , Proceedings of the International Conference on Image Analysis and Recognition, Springer, Berlin; 
Grandjean, O., Vernoux, T., Laufs, P., Belcram, K., Mizukami, Y., Traas, J., In vivo analysis of cell division, cell growth, and differentiation at the shoot apical meristem in Arabidopsis (2004) Plant Cell, 16 (1), pp. 74-87; 
Iwamoto, A., Satoh, D., Furutani, M., Maruyama, S., Ohba, H., Sugiyama, M., Insight into the basis of root growth in Arabidopsis thaliana provided by a simple mathematical model (2006) Journal of Plant Research, 119, pp. 85-93; 
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J., On combining classifiers (1998) IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 (3), pp. 226-239; 
P. Kovesi, Symmetry and asymmetry from local phase, in: Proceedings of the 10th Australian Joint Conference on Artificial Intelligence, December 1997, pp. 185-190P. Kovesi, Edges are not just steps, in: Proceedings of the Fifth Asian Conference on Computer Vision, January 2002, pp. 822-827Liao, L., Noble, W.S., Combining pairwise sequence similarity and support vector machines for remote protein homology detection (2002) Proceedings of the Sixth Annual International Conference on Computational Molecular Biology, pp. 225-232. , ACM Press; 
Luc, V., Soille, P., Watersheds in digital spaces: an efficient algorithm based on immersion simulations (1991) IEEE Transactions on Pattern Analysis and Machine Intelligence, 13 (6), pp. 583-598; 
Mao, K., Zhao, P., Tan, P.-H., Supervised learning-based cell image segmentation for p53 immunohistochemistry (2006) IEEE Transactions on Biomedical Engineering, 53 (6), pp. 1153-1163; 
M. Marcuzzo, P. Quelhas, A. Campilho, A.M. Mendon{\c c}a, A.C. Campilho, Automatic cell segmentation from confocal microscopy images of the Arabidopsis root, in: IEEE International Symposium on Biomedical Imaging, May 2008, pp. 712-715Marcuzzo, M., Quelhas, P., Campilho, A., Mendon{\c c}a, A.M., Campilho, A.C., A hybrid approach for cell image segmentation (2008) Springer Lecture Notes in Computer Science, 5112, pp. 739-749. , Proceedings of the International Conference on Image Analysis and Recognition, Springer, Berlin; 
Meyer, F., Beucher, S., Morphological segmentation (1990) Journal of Visual Communication and Image Representation, 1 (1), pp. 21-46; 
Perona, P., Malik, J., Scale-space and edge detection using anisotropic diffusion (1990) IEEE Transactions on Pattern Analysis and Machine Intelligence, 7 (12), pp. 629-639; 
T. Roberts, S. Mckenna, N. Wuyts, T. Valentine, A. Bengough, Performance of low-level motion estimation methods for confocal microscopy of plant cells in vivo, in: IEEE Workshop on Motion \& Video Computing, February 2007, pp. 13-21Sathiya Keerthi, S., Lin, C.-J., Asymptotic behaviors of support vector machines with Gaussian kernel (2003) Neural Computation, 15 (7), pp. 1667-1689; 
Vapnik, V., (1998) Statistical Learning Theory, , Wiley, New York
},
	month = {2009///},
	pages = {785 - 793},
	abstract = {To obtain development information of individual plant cells, it is necessary to perform in vivo imaging of the specimen under study, through time-lapse confocal microscopy. Automation of cell detection/marking process is important to provide research tools in order to ease the search for special events, such as cell division. In this paper we discuss an automatic cell detection approach for Arabidopsis thaliana based on segmentation, which selects the best cell candidates from a starting watershed-based image segmentation and improves the result by merging adjacent regions. The selection of individual cells is obtained using a support vector machine (SVM) classifier, based on a cell descriptor constructed from the shape and edge strength of the cells{\textquoteright} contour. In addition we proposed a novel cell merging criterion based on edge strength along the line that connects adjacent cells{\textquoteright} centroids, which is a valuable tool in the reduction of cell over-segmentation. The result is largely pruned of badly segmented and over-segmented cells, thus facilitating the study of cells. When comparing the results after merging with the basic watershed segmentation, we obtain 1.5\% better coverage (increase in F-measure) and up to 27\% better precision in correct cell segmentation. {\textcopyright} 2009 Elsevier Ltd. All rights reserved.},
	keywords = {accuracy, Arabidopsis, Arabidopsis plant, Arabidopsis thaliana, article, artificial intelligence, automation, Bioinformatics, Biological cells, Biomedical image processing, Cell detection, Cell divisions, Cell membranes, Cell merging, cell proliferation, Cell segmentation, Cell Shape, Cell Wall, Computer simulation, confocal microscopy, controlled study, Descriptor, F-measure, Image analysis, image processing, Image Processing, Computer-Assisted, Image segmentation, Imaging systems, In-Vivo imaging, Individual cells, Landforms, Merging, Models, Biological, nonhuman, Over segmentation, Plant cells, plant root, Plant roots, Plants (botany), priority journal, Region-merging, Research tools, root morphology, Segmented cell, signal noise ratio, support vector machine, Support vector machines, SVM classification, Watershed segmentation, Watersheds},
	isbn = {00104825 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-67849119398\&partnerID=40\&md5=8029bce67aff136ccc5e0f7e4f9e0ded},
	author = {Marcuzzo, M. and Quelhas, P. and Campilho, A. and Maria Mendon{\c c}a, A. and Campilho, A.}
}
@book {2302,
	title = {Cell division detection on the arabidopsis thaliana root},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {5524 LNCS},
	year = {2009},
	note = {Conference code: 77027Cited By (since 1996): 5Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-02172-5_23Language of Original Document: EnglishCorrespondence Address: Marcuzzo, M.; INEB - Instituto de Engenharia Biom{\'e}dica, Divis{\~a}o de Sinal e Imagem, Campus FEUPReferences: Campilho, A., Garcia, B., Toorn, H., Wijk, H., Campilho, A., Scheres, B., Time-lapse analysis of stem-cell divisions in the arabidopsis thaliana root meristem (2006) The Plant Journal, 48, pp. 619-627;Iwamoto, A., Satoh, D., Furutani, M., Maruyama, S., Ohba, H., Sugiyama, M., Insight into the basis of root growth in arabidopsis thaliana provided by a simple mathematical model (2006) J. Plant Res, 119, pp. 85-93; 
Roberts, T., Mckenna, S., Wuyts, N., Valentine, T., Bengough, A.: Performance of low-level motion estimation methods for confocal microscopy of plant cells in vivo. In: Motion 2007, p. 13 (2007)Beemster, G., Baskin, T., Analysis of cell division and elongation underlying the developmental acceleration of root growth in arabidopsis thaliana (1998) Plant Physiology, 116, pp. 1515-1526; 
Chen, X., Zhou, X., Wong, S.T.C., Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy (2006) IEEE Trans. on Biomedical Engineering, 53 (4), pp. 762-766; 
Mao, K.Z., Zhao, P., Tan, P., Supervised learning-based cell image segmentation for p53 immunohistochemistry (2006) IEEE Trans. on Biomedical Engineering, 53 (6), pp. 1153-1163; 
Yang, X., Li, H., Zhou, X., Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and kalman filter in time-lapse microscopy (2006) IEEE Trans. on Circuits and Systems I-Regul. Papers, 53 (11), pp. 2405-2414; 
Harder, N., M-Bermudez, F., Godinez, W., Ellenberg, J., Eils, R., Rohr, K., Automated analysis of mitotic cell nuclei in 3d fluorescence microscopy image sequences (2008) Workshop on Bio-Image Informatics: Biological Imaging, Computer Vision and Data Mining; 
Kobatake, H., Hashimoto, S., Convergence index filter for vector fields (1999) IEEE Trans. on Image Processing, 8 (8); 
Wei, J., Hagihara, Y., Kobatake, H., Detection of rounded opacities on chest radiographs using convergence index filter (1999) Proceedings of the Int. Conference on Image Analysis and Processing, pp. 757-761; 
Pereira, C.S., Fernandes, H., Mendon{\c c}a, A.M., Campilho, A.C.: Detection of lung nodule candidates in chest radiographs. In: Mart{\'\i}, J., Bened{\'\i}, J.M., Mendon{\c c}a, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, 4478, pp. 170-177. Springer, Heidelberg (2007)Marcuzzo, M., Quelhas, P., Campilho, A., Mendon{\c c}a, A.M., Campilho, A., A hybrid approach for cell image segmentation (2008) LNCS, 5112, pp. 739-749. , Campilho, A, Kamel, M.S, eds, ICIAR 2008, Springer, Heidelberg
Sponsors: International Association for Pattern Recognition (IAPR); Fundacao para a Ciencia e a Tecnologia (FCT)},
	month = {2009///},
	pages = {168 - 175},
	address = {Povoa de Varzim},
	abstract = {The study of individual plant cells and their growth structure is an important focus of research in plant genetics. To obtain development information at cellular level, researchers need to perform in vivo imaging of the specimen under study, through time-lapse confocal microscopy. Within this research field it is important to understand mechanisms like cell division and elongation of developing cells. We describe a tool to automatically search for cell division in the Arabidopsis thaliana using information of nuclei shape. The nuclei detection is based on a convergence index filter. Cell division detection is performed by an automatic classifier, trained through cross-validation. The results are further improved by a stability criterion based on the Mahalanobis distance of the shape of the nuclei through time. With this approach, we can achieve a correct detection rate of 94.7\%. {\textcopyright} 2009 Springer Berlin Heidelberg.},
	keywords = {A-stability, Arabidopsis thaliana, Automatic classifiers, Bioinformatics, Cell divisions, Cell membranes, cell proliferation, Cellular levels, confocal microscopy, Cross validation, Detection rates, Growth structures, Image analysis, In-plants, In-Vivo imaging, Mahalanobis distances, Pattern recognition, Plant cells, research, Research fields, Stability criteria},
	isbn = {03029743 (ISSN); 3642021719 (ISBN); 9783642021718 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-68749115481\&partnerID=40\&md5=2c491950d3b00a9dd873aeaefdf5edcb},
	author = {Marcuzzo, M. and Guichard, T. and Quelhas, P. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@book {2301,
	title = {Chromatographic pattern recognition using optimized one-class classifiers},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {5524 LNCS},
	year = {2009},
	note = {Conference code: 77027Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-02172-5_58Language of Original Document: EnglishCorrespondence Address: Sousa, A. V.; Instituto de Engenharia Biom{\'e}dica, Rua Roberto Frias, Porto 4200-465, Portugal; email: ats@isep.ipp.ptReferences: Skurichina, M., Raudys, S., Duin, R.P.W., K-Nearest Neighbors Directed Noise Injection in Multilayer Perceptron Training (2000) IEEE Transactions on Neural Networks, 11;Chawla, N.V., Japkowicz, N., Kolcz, A., Editorial: Special Issue: Learning from Imbal-anced Data Sets (2004) Sigkdd Explorations, 6, pp. 1-6; 
Tax, D.M.J., (2001) One-class Classification; Concept-learning in the Absence of Counterexamples, , Ph.D. thesis: Delft University of Technology; 
Durand, G., Seta, N., Protein Glycosylation and Diseases: Blood and Urinary Oligosac-charides as Markers for Diagnosis and Therapeutic Monitoring (2000) Clin. Chem, 46, pp. 795-805; 
Sewell, A.C., Urinary Oligosaccharides. Techniques in Diagnostic Human (1991) Biochemical Genetics, pp. 219-231; 
Pekalska, E., Duin, R.P.W., Dissimilarity Representations Allow for Building Good Classifiers (2002) Pattern Recogn. Lett, 23, pp. 943-956; 
Sousa, A.V., Mendon{\c c}a, A.M., Campilho, A., Dissimilarity-based Classification of Chromatographic Profiles (2008) Pattern Analysis and Applications, 11 (3-4), pp. 409-423; 
Sousa, A.V., Mendon{\c c}a, A.M., Campilho, A., Minimizing the Imbalance Problem in Chromatographic Profile Classification with One-class Classifiers (2008) LNCS, 5112, pp. 413-422. , Campilho, A, Kamel, M.S, eds, ICIAR 2008, Springer, Heidelberg; 
Sousa, A.V., Mendon{\c c}a, A.M., Campilho, A., Chromatographic Pattern Classification (2008) IEEE Trans. on Biomedical Engineering, 55 (6), pp. 1687-1696; 
Visa, S., Ralescu, A., Learning Imbalanced and Overlapping Classes Using Fuzzy Sets (2003) Workshop on Learning from Imbalanced Datasets II, ICML, , Washington DC
Sponsors: International Association for Pattern Recognition (IAPR); Fundacao para a Ciencia e a Tecnologia (FCT)},
	month = {2009///},
	pages = {449 - 456},
	address = {Povoa de Varzim},
	abstract = {This paper presents a method for automating the selection of the rejection rate of one-class classifiers aiming at optimizing the classifier performance. These classifiers are used in a new classification approach to deal with class imbalance in Thin-Layer Chromatography (TLC) patterns, which is due to the huge difference between the number of normal and pathological cases, as a consequence of the rarity of Lysosomal Storage Disorders (LSD) diseases. The classification is performed in two decision stages, both implemented using optimized one-class classifiers: the first stage aims at recognizing most of the normal samples; the outliers of this first decision level are presented to the second stage, which is a multiclassifier prepared to deal with both pathological and normal patterns. The results that were obtained proved that the proposed methodology is able to overcome some of the difficulties associated with the choice of the rejection rate of one-class classifiers, and generally contribute to the minimization of the imbalance problem in TLC pattern classification. {\textcopyright} 2009 Springer Berlin Heidelberg.},
	keywords = {Chromatographic analysis, Class imbalance, Classification approach, Classifier performance, Classifiers, Decision levels, Image analysis, Imbalance problem, Learning systems, Lysosomal storage disorder, Multi-classifier, One-class classifier, Optimization, Pathological case, Pattern classification, Pattern recognition, Rejection rates, Thin layer chromatography},
	isbn = {03029743 (ISSN); 3642021719 (ISBN); 9783642021718 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-68749107350\&partnerID=40\&md5=17cc4075de5306819dcd1bfc005171d6},
	author = {Sousa, A.V. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@book {2295,
	title = {Evaluation of symmetry enhanced sliding band filter for plant cell nuclei detection in low contrast noisy fluorescent images},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {5627 LNCS},
	year = {2009},
	note = {Conference code: 77746Cited By (since 1996): 3Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-02611-9_81Language of Original Document: EnglishCorrespondence Address: Marcuzzo, M.; INEB - Instituto de Engenharia Biom{\'e}dica Divis{\~a}o de Sinal e Imagem, Campus FEUPReferences: Campilho, A., Garcia, B., Toorn, H., Wijk, H., Campilho, A., Scheres, B., Time-lapse analysis of stem-cell divisions in the arabidopsis thaliana root meristem (2006) The Plant Journal, 48, pp. 619-627;Iwamoto, A., Satoh, D., Furutani, M., Maruyama, S., Ohba, H., Sugiyama, M., Insight into the basis of root growth in arabidopsis thaliana provided by a simple mathematical model (2006) J. Plant Res, 119, pp. 85-93; 
Roberts, T., Mckenna, S., Wuyts, N., Valentine, T., Bengough, A.: Performance of low-level motion estimation methods for confocal microscopy of plant cells in vivo. In: Motion 2007, p. 13 (2007)Beemster, G., Baskin, T., Analysis of cell division and elongation underlying the developmental acceleration of root growth in arabidopsis thaliana (1998) Plant Physiology, 116, pp. 1515-1526; 
Willemse, J., Kulikova, O., de Jong, H., Bisseling, T., A new whole-mount dna quantification method and the analysis of nuclear dna content in the stem-cell niche of arabidopsis roots (2008) The Plant Journal, 10, pp. 1365-1374; 
Chen, X., Zhou, X., Wong, S.T.C., Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy (2006) IEEE Trans. on Biomedical Engineering, 53 (4), pp. 762-766; 
Mao, K.Z., Zhao, P., Tan, P., Supervised learning-based cell image segmentation for p53 immunohistochemistry (2006) IEEE Trans. on Biomedical Engineering, 53 (6), pp. 1153-1163; 
Schmitt, M.H.O., Morphological multiscale decomposition of connected regions with emphasis on cell clusters (2009) Computer Vision and Image Understanding, 113, pp. 188-201; 
Yang, X., Li, H., Zhou, X., Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and kalman filter in time-lapse microscopy (2006) IEEE Trans. on Circuits and Systems I-Regul. Papers, 53 (11), pp. 2405-2414; 
Kobatake, H., Hashimoto, S., Convergence index filter for vector fields (1999) IEEE Trans. on Image Processing, 8 (8); 
Wei, J., Hagihara, Y., Kobatake, H., Detection of rounded opacities on chest radiographs using convergence index filter (1999) Proceedings of the Int. Conference on Image Analysis and Processing, pp. 757-761; 
},
	month = {2009///},
	pages = {824 - 831},
	address = {Halifax, NS},
	abstract = {The study of cell nuclei is fundamental for plant cell Biology research. To obtain information at cellular level, researchers image cells{\textquoteright} nuclei which were modified with fluorescence proteins, through laser scanning confocal microscopy. These images are normally noisy and suffer from high background fluorescence, making grey-scale segmentation approaches inadequate for a usable detection. To obtain a successful detection even at low contrast we investigate the use of a particular convergence filter, the Symmetric Sliding Band filter (SSBF), for cell detection. This filter is based on gradient convergence and not intensity. As such it can detect low contrast cell nuclei which otherwise would be lost in the background noise. Due to the characteristics of cell nuclei morphology, a symmetry constrain is integrated in the filter which corrects some inadequate detections and results in a filter response that is more discriminative. We evaluate the use of this filter for cell nuclei detection on the Arabidopsis thaliana root tip, where the nuclei were stained using yellow fluorescence protein. The resulting cell nuclei detection precision is 89\%. {\textcopyright} 2009 Springer Berlin Heidelberg.},
	keywords = {Arabidopsis thaliana, Background noise, Band filters, Bioinformatics, Biology imaging, Cell detection, Cell membranes, cell nucleus, Cellular levels, confocal microscopy, Convergence filters, cytology, Filter response, Fluorescence, Fluorescence proteins, Fluorescent images, Image analysis, Laser scanning confocal microscopy, Low contrast, Plant cell culture, Plant cells, Positron emission tomography, Root tip, Yellow fluorescence proteins},
	isbn = {03029743 (ISSN); 3642026109 (ISBN); 9783642026102 (ISBN)},
	author = {Marcuzzo, M. and Quelhas, P. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@conference {2338,
	title = {Automatic cell segmentation from confocal microscopy images of the Arabidopsis root},
	booktitle = {2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBIIEEE Int. Symp. Biomed. Imaging: Nano Macro, Proc., ISBI},
	series = {2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI},
	year = {2008},
	note = {Conference code: 73366Cited By (since 1996): 4Export Date: 21 November 2012Source: ScopusArt. No.: 4541095doi: 10.1109/ISBI.2008.4541095Language of Original Document: EnglishCorrespondence Address: Marcuzzo, M.; INEB - Instituto de Engenharia Biom{\'e}dica, Divis{\~a}o de Sinal e Imagem, Campus FEUPPortugalReferences: Campilho, A., Garcia, B., Toorn, H., Wijk, H., Campilho, A., Scheres, B., Time-lapse analysis of stem-cell divisions in the arabidopsis thaliana root meristem (2006) The Plant Journal, 48, pp. 619-627;Iwamoto, A., Satoh, D., Furutani, M., Maruyama, S., Ohba, H., Sugiyama, M., Insight into the basis of root growth in arabidopsis thaliana provided by a simple mathematical model (2006) J. Plant Res, 119, pp. 85-93; 
Roberts, T., Mckenna, S., Wuyts, N., Valentine, T., Bengough, A., Performance of low-level motion estimation methods for confocal microscopy of plant cells in vivo (2007) Motion07, pp. 13-13; 
Beemster, G., Baskin, T., Analysis of cell division and elongation underlying the developmental acceleration of root growth in arabidopsis thaliana (1998) Plant Physiology, 116, pp. 1515-1526; 
Garcia, B., Campilho, A., Scheres, B., Campilho, A., Automatic tracking of arabidopsis thaliana root meristem in confocal microscopy (2004) Proc. of the Inter. Conf. on Image Analysis and Recognition, pp. 166-174; 
Perona, P., Malik, J., Scale-space and edge detection using anisotropic diffusion (1990) IEEE Trans. on PAMI, 7 (12), pp. 629-639; 
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K., Image denoising by sparse 3d transform-domain collaborative filtering (2007) IEEE Trans. on Image Processing, 16 (8), pp. 2080-2095. , August; 
Luc, V., Soille, P., Watersheds in digital spaces: An efficient algorithm based on immersion simulations IEEE Trans. on PAMI, 13 (6); 
Meyer, F., Beucher, S., Morphological segmentation (1990) JVCIR, 1 (1), pp. 21-46. , Sept; 
Bleau, A., Leon, L., Watershed-based segmentation and region merging (2000) Comput. Vis. Image Underst, 77 (3), pp. 317-370; 
Kovesi, P., Symmetry and asymmetry from local phase (1997) Proc. of the Tenth Australian Joint Conf. on Artificial Intelligence, , December; 
Burges, C., A tutorial on support vector machines for pattern recognition (1998) Data Mining and Knowledge Discovery, 2 (2), pp. 121-167
Sponsors: Institute of Electrical and Electronic Engineers; Engineering in Medicine and Biology Society; Signal Processing Society},
	month = {2008///},
	pages = {712 - 715},
	address = {Paris},
	abstract = {In vivo observation and tracking of cell division in the Arabidopsis thaliana root meristem, by time-lapse confocal microscopy, is central to biology research. The research herein described is based on large amount of image data, which must be analyzed to determine the location and state of cells. The possibility of automating the process of cell detection/marking is an important step to provide research tools to the biologists in order to ease the search for a special event as cell division. This paper discusses an automatic cell segmentation method, which selects the best cell candidates from a starting watershed based image segmentation. The selection of individual cells is obtained using a Support Vector Machine (SVM) classifier, based on the shape and edge strength of the cells{\textquoteright} contour. The resulting segmentation is largely pruned of badly segmented cells, which can reduce the false positive detection of cell division. This is a good result on its own and a starting point for improvement of cell segmentation methodology. {\textcopyright}2008 IEEE.},
	keywords = {Biological cells, Biomedical image processing, Cell divisions, cell proliferation, Cell segmentation, Cells, confocal microscopy, image processing, Image segmentation, Microscopic examination, Support vector machines, Technical presentations},
	isbn = {9781424420032 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-51049123600\&partnerID=40\&md5=9e4652fa61936d105b4059db95e63f78},
	author = {Marcuzzo, M. and Quelhas, P. and Campilho, A. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@article {2348,
	title = {Chromatographic pattern classification},
	journal = {IEEE Transactions on Biomedical EngineeringIEEE Trans. Biomed. Eng.},
	volume = {55},
	year = {2008},
	note = {Cited By (since 1996): 5Export Date: 21 November 2012Source: ScopusArt. No.: 8CODEN: IEBEAdoi: 10.1109/TBME.2008.919852PubMed ID: 18714832Language of Original Document: EnglishCorrespondence Address: Sousa, A. V.; Instituto de Engenharia Biom{\'e}dica (INEB), Instituto Superior de Engenharia Do Porto, 4200-465 Porto, Portugal; email: ats@isep.ipp.ptChemicals/CAS: Hydrolases, EC 3.-References: Keir, G., Winchester, B., Clayton, P., Carbohydrate-deficient glycoprotein syndromes: Inborn errors of protein glycosylation (1999) Ann. Clin. Biochem, 36 (1), pp. 20-36;Durand, G., Seta, N., Protein glycosylation and diseases: Blood and urinary oligosaccharides as markers for diagnosis and therapeutic monitoring (2000) Clin. Chem, 46, pp. 795-805; 
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Pinto, R., Caseiro, C., Lemos, M., Lopes, L., Fontes, A., Miranda, M., Prevalence of lysosomal storage diseases in Portugal (2004) Eur. J. Hum. Genet, 12, pp. 87-92; 
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Duvdevani-Bar, S., Edelman, S., Visual recognition and categorization on the basis of similarities to multiple class prototypes (1999) Int. J. Comput. Vis, 33, pp. 201-228; 
Pekalska, E., Duin, R., Dissimilarity representations allow for building good classifiers (2002) Pattern Recognit. Lett, 23, pp. 943-956; 
Goldstone, R., Son, J., Similarity (2005) Cambridge Handbook of Thinking and Reasoning, pp. 13-36. , K. Holyoak and R. Morrison, Eds. Cambridge, MA: Cambridge Univ. Press; 
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Schwedt, G., (1997) The Essential Guide to Analytical Chemistry, , New York: Wiley; 
Bajla, I., Hollander, I., Fluch, S., Burg, K., Kollar, M., An alternative method for electrophoretic gel image analysis in the GelMaster software (2005) Comput. Methods Programs Biomed, 77, pp. 209-231; 
Eibrand, R., Kennedy, P., Cotter, D., MacEvilly, U., Wu, B., Analysis of Atlantic salmon skin mucus: COPS - A computer-based system for protein pattern analysis of 1D SDS-PAGE gels (2003) Proc. IEEE Symp. Bioinf. BioEng, pp. 45-52; 
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Ye, X., Suen, C., Cheriet, M., Wang, E., A recent development in image analysis of electrophoresis gels (1999) Proc. Vis. Interface, pp. 432-438; 
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Bajla, I., Hollander, I., Burg, K., Improvement of electrophoretic gel image analysis (2001) Meas. Sci. Rev, 1, pp. 5-10; 
Bajla, I., Hollander, I., Burg, K., Fluch, S., A novel approach to quantitative analysis of electrophoretic gel images of DNA fragments (2002) Proc. IEEE Int. Symp. Biomed. Imag, pp. 899-902; 
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Beltr{\'a}n, N., Duarte-Mermoud, M., Salah, S., Bustos, M., Feature selection algorithms using Chilean wine chromatograms as examples (2005) J. Food Eng, 67, pp. 483-490; 
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Mor{\'e}, J., Sorensen, D., Computing a trust region step (1983) SIAM J. Sci. Stat. Comput, 4, pp. 553-572; 
Byrd, R., Schnabel, R., Shultz, G., Approximate solution of the trust region problem by minimization over two-dimensional subspaces (1988) Math. Program, 40, pp. 247-263; 
Sousa, A., Mendon{\c c}a, A., Campilho, A., Aguiar, R., Miranda, C., Feature extraction for classification of thin-layer chromatography images (2005) Lecture Notes in Computer Science, 3656, pp. 974-981. , Image Analysis and Recognition, New York: Springer-Verlag; 
Ripley, B., (2004) Pattern Recognition and Neural Networks, , New York: Cambridge Univ. Press; 
Visa, S., Ralescu, A., Learning imbalanced and overlapping classes using fuzzy sets (2003) ICML 2003, Workshop Learn. Imbalanced Datasets, 2. , presented at the, Washington, DC
},
	month = {2008///},
	pages = {1687 - 1696},
	abstract = {In this paper, we propose and evaluate methodologies for the classification of images from thin-layer chromatography. Each individual sample is characterized by an intensity profile that is further represented into a feature space. The first steps of this process aim at obtaining a robust estimate of the intensity profile by filtering noise, reducing the influence of background changes, and by fitting a mixture of Gaussians. The resulting profiles are represented by a set of appropriate features trying to characterize the state of nature, here spread out over four classes, one for normal subjects and the other three corresponding to lysosomal diseases, which are disorders responsible for severe nerve degeneration. For classification purposes, a novel solution based on a hierarchical structure is proposed. The main conclusion of this paper is that an automatically generated decision tree presents better results than more conventional solutions, being able to deal with the natural imbalance of the data that, as consequence of the rarity of lysosomal disorders, has very few representative cases in the disease classes when compared with the normal population. {\textcopyright} 2008 IEEE.},
	keywords = {Acoustic variables control, Algorithms, article, Chromatographic analysis, Chromatographic pattern features, Chromatographic profiles, Chromatography, Chromatography, Thin Layer, Classification, Diagnosis, Computer-Assisted, Gaussian noise (electronic), Hierarchical classifier, Humans, Hydrolases, imaging, Lysosomal Storage Diseases, lysosome storage disease, mathematical analysis, nerve degeneration, noise, normal distribution, Pattern recognition, Pattern Recognition, Automated, Reproducibility of Results, sensitivity and specificity, Urinalysis},
	isbn = {00189294 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-44349183146\&partnerID=40\&md5=df677f44d528c936310d8c547624a550},
	author = {Sousa, A.V. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@article {2340,
	title = {Dissimilarity-based classification of chromatographic profiles},
	journal = {Pattern Analysis and ApplicationsPattern Anal. Appl.},
	volume = {11},
	year = {2008},
	note = {Cited By (since 1996): 3Export Date: 21 November 2012Source: Scopusdoi: 10.1007/s10044-008-0113-2Language of Original Document: EnglishCorrespondence Address: Sousa, A. V.; Instituto Superior de Engenharia do Porto, Rua Dr. Ant{\'o}nio Bernardino Almeida, 431, 4200-072 Porto, Portugal; email: ats@isep.ipp.ptReferences: Reich, E., Blatter, A., Modern TLC: A key technique for identification and quality control of botanicals and dietary supplements (2004) Inside Laboratory Management-AOC International;Sousa, A.V., Aguiar, R., Mendon{\c c}a, A.M., Campilho, A., Automatic lane and band detection in images of thin layer chromatography (2004) LNCS, 3212, pp. 158-165. , Proceedings of image analysis and recognition: international conference, ICIAR 2004, Porto Springer, Heidelberg; 
Keir, G., Winchester, B.G., Clayton, P., Carbohydrate-deficient glycoprotein syndromes: Inborn errors of protein glycosylation (1999) Ann Clin Biochem, 36, pp. 20-36. , Pt 1; 
Durand, G., Seta, N., Protein glycosylation and diseases: Blood and urinary oligosaccharides as markers for diagnosis and therapeutic monitoring (2000) Clin Chem, 46, pp. 795-805; 
Schwedt, G., (1997) The Essential Guide to Analytical Chemistry, , Wiley New York; 
Bajla, I., Hollander, I., Fluch, S., Burg, K., Kollar, M., An alternative method for electrophoretic gel image analysis in the GelMaster software (2005) Comput Methods Programs Biomed, 77, pp. 209-231; 
Eibrand, R., Kennedy, P., Cotter, D., MacEvilly, U., Wu, B., Analysis of atlantic salmon skin mucus: COPS-a computer-based system for protein pattern analysis of 1D SDS-PAGE gels (2003) Proceedings of the Third IEEE Symposium on Bioinformatics and Bioengeneering; 
Gerasimov, A.V., Use of the software processing of scanned chromatogram images in quantitative planar chromatography (2004) J Anal Chem, 59, pp. 348-353; 
(2000) Gel Analysis Software: Important Aspects, , Goulding electrophoresis PN UVItec Ltd; 
Jedra, M., El Khattabi, N., Limouri, M., Essaid, A., Recognition of seed varieties using a time-delay neural network: Analysis of electrophoretic images (1999) Comput Electron Agric, 22, pp. 1-10; 
MacHado, A.M.C., Campos, M.F.M., Siqueira, A.M., De Carvalho, O.S.F., An iterative algorithm for segmenting lanes in gel electrophoresis images. Computer graphics and image processing (1997) Proceedings of X Brazilian Symposium, pp. 140-146; 
Ye, X., Suen, C.Y., Cheriet, M., Wang, E., A recent development in image analysis of electrophoresis gels (1999) Vision Iterface 99, , Trois-Rivieres, Canada; 
Wang, D., Keller, J.M., Carson, C.A., Pulsed-field gel electrophoresis pattern recognition of bacterial DNA: A systemic approach (2001) Pattern Anal Appl, 4, pp. 244-255; 
Shadle, S.E., Allen, D.F., Guo, H., Pogozelski, W.K., Bashkin, J.S., Tullius, T.D., Quantitative analysis of electrophoresis data: Novel curve fitting methodology and its application to the determination of a protein-NA binding constant (1997) Nucleic Acids Res, 25, pp. 850-860; 
Bajla, I., Hollander, I., Burg, K., Improvement of electrophoretic gel image analysis (2001) Measurement Science Review, 1; 
Bajla, I., Hollander, I., Burg, K., Fluch, S., (2002) A Novel Approach to Quantitative Analysis of Electrophoretic Gel Images of DNA Fragments, pp. 899-902. , IEEE international symposium on biomedical imaging Washington; 
Lavine, B.K., Meyers, R.A., Clustering and classification of analytical data (2000) Encyclopedia of Analytical Chemistry: Instrumentation and Applications, pp. 9689-9710. , Wiley Chichester; 
Lonni, A., Scarminio, I., Silva, L., Ferreira, D., Differentiation of species of bacharis genus by HPLC and chemometrics methods (2003) Analytical Sciences, 19; 
Beltr{\'a}n, N.H., Duarte-Mermoud, M.A., Salah, S.A., Bustos, M.A., Feature selection algorithms using Chilean wine chromatograms as examples (2005) J Food Eng, 67, pp. 483-490; 
Landgrebe, D., Information extraction principles and methods for multispectral and hyperspectral image data (1999) Proceedings of Information Processing for Remote Sensing, , World Scientific, Singapore; 
Landgrebe, D., Hyperspectral image data analysis as a high dimensional signal processing problem (2002) IEEE Signal Process Mag, 19, pp. 17-28; 
Jimenez, L., Landgrebe, D., Hyperspectral data analysis and feature reduction via projection pursuit (1999) IEEE Trans Geosci Remote Sens, 37, pp. 2653-2667; 
Jimenez, L.O., Landgrebe, D.A., Supervised classification in high-dimensional space: Geometrical, statistical, and asymptotical properties of multivariate data (1998) IEEE Transaction on Systems Man, and Cybernetics-Part C: Applications and Reviews, 28; 
Paclik, P., Duin, R.P.W., Dissimilarity-based classification of spectra: Computational issues (2003) Real-Time Imaging, 9, pp. 237-244; 
Paclik, P., Duin, R.P.W., Classifying spectral data using relational representation (2003) Proceedings of Spectral Imaging Workshop, , Graz; 
Pekalska, E., Paclik, P., Duin, R.P.W., A generalized kernel approach to dissimilarity-based classification (2001) J Mach Learn Res, 2, pp. 175-211; 
Pekalska, E., Duin, R.P.W., Dissimilarity representations allow for building good classifiers (2002) Pattern Recogn Lett, 23, pp. 943-956; 
Pekalska, E., Duin, R.P.W., (2005) The Dissimilarity Representation for Pattern Recognition-foundations and Applications, 64. , World Scientific, Singapore; 
Pekalska, E., Duin, R.P.W., Paclik, P., Prototype selection for dissimilarity-based classifiers (2006) Pattern Recogn, 39, pp. 189-208; 
Harol, A., Lai, C., Pekalska, E., Duin, R.P.W., Pairwise feature evaluation for constructing reduced representations (2007) Pattern Anal Appl, 10, pp. 55-68; 
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Dasarathy, B.V., S{\'a}nchez, J.S., Townsend, S., Nearest neighbour editing and condensing tools-synergy exploitation (2000) Pattern Anal Appl, 3, pp. 19-30; 
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Vijaya, P.A., Murty, M.N., Subramanian, D.K., Efficient median based clustering and classification techniques for protein sequences (2006) Pattern Anal Appl, 9, pp. 243-255; 
Sousa, A.V., Mendon{\c c}a, A.M., Campilho, A., Aguiar, R., Miranda, C.S., Feature extraction for classification of thin-layer chromatography images (2005) Proceedings of Image Analysis and Recognition, Second International Conference, ICIAR 2005, 3656, pp. 974-981. , LNCS Springer, Toronto; 
Dempster, A., Laird, N., Rubin, D., Maximum likelihood from incomplete data via the em algorithm (1977) J R Stat Soc, B-39, pp. 1-38. , 1; 
Mor{\'e}, J.J., Sorensen, D.C., Computing a trust region step (1983) SIAM J Sci Stat Comput, 4, pp. 553-572; 
Byrd, R.H., Schnabel, R.B., Shultz, G.A., Approximate solution of the trust region problem by minimization over two-dimensional subspaces (1988) Math Program, 40, pp. 247-263; 
Visa, S., Ralescu, A., Learning imbalanced and overlapping classes using fuzzy sets (2003) Workshop on Learning from Imbalanced Datasets II, , ICML, Washington DC; 
Sousa, A.V., Mendon{\c c}a, A.M., Campilho, A.C., The class imbalance problem in TLC image classification (2006) Proceedings of Image Analysis and Recognition, Third International Conference, ICIAR 2006, 4142, pp. 513-523. , September 18-20, 2006, proceedings, Part II, P{\'o}voa de Varzim. LNCS Springer, Portugal
},
	month = {2008///},
	pages = {409 - 423},
	abstract = {This paper proposes a non-parametric method for the classification of thin-layer chromatographic (TLC) images from patterns represented in a dissimilarity space. Each pattern corresponds to a mixture of Gaussian approximation of the intensity profile. The methodology comprises various phases, including image processing and analysis steps to extract the chromatographic profiles and a classification phase to discriminate among two groups, one corresponding to normal cases and the other to three pathological classes. We present an extensive study of several dissimilarity-based approaches analysing the influence of the dissimilarity measure and the prototype selection method on the classification performance. The main conclusions of this paper are that, Match and Profile-difference dissimilarity measures present better results, and a new prototype selection methodology achieves a performance similar or even better than conventional methods. Furthermore, we also concluded that simplest classifiers, such as k-NN and linear discriminant classifiers (LDCs), present good performance being the overall classification error less than 10\% for the four-class problem. {\textcopyright} 2008 Springer-Verlag London Limited.},
	keywords = {Chromatographic images, Dissimilarity representation, Nearest neighbour classifiers, Prototype selection},
	isbn = {14337541 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-50549105086\&partnerID=40\&md5=2a5bd02f0caacb5e7ccb1267a861db75},
	author = {Sousa, A.V. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@book {2346,
	title = {A hybrid approach for arabidopsis root cell image segmentation},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {5112 LNCS},
	year = {2008},
	note = {Conference code: 72695Cited By (since 1996): 3Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-540-69812-8_73Language of Original Document: EnglishCorrespondence Address: Marcuzzo, M.; INEB - Instituto de Engenharia Biom{\'e}dica, Divis{\~a}o de Sinal e Imagem, Campus FEUPPortugalReferences: Campilho, A., Garcia, B., Toorn, H., Wijk, H., Campilho, A., Scheres, B., Time-lapse analysis of stem-cell divisions in the arabidopsis thaliana root meristem (2006) The Plant Journal, 48, pp. 619-627;Iwamoto, A., Satoh, D., Furutani, M., Maruyama, S., Ohba, H., Sugiyama, M., Insight into the basis of root growth in arabidopsis thaliana provided by a simple mathematical model (2006) J. Plant Res, 119, pp. 85-93; 
Roberts, T., Mckenna, S., Wuyts, N., Valentine, T., Bengough, A.: Performance of low-level motion estimation methods for confocal microscopy of plant cells in vivo. In: Motion 2007, pp. 13-13 (2007)Beemster, G., Baskin, T., Analysis of cell division and elongation underlying the developmental acceleration of root growth in arabidopsis thaliana (1998) Plant Physiology, 116, pp. 1515-1526; 
Garcia, B., Campilho, A., Scheres, B., Campilho, A., Automatic Tracking of Arabidopsis thaliana Root Meristem in Confocal Microscopy (2004) LNCS, 3212, pp. 166-174. , Campilho, A.C, Kamel, M, eds, ICIAR 2004, Springer, Heidelberg; 
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Meyer, F., Beucher, S., Morphological segmentation (1990) JVCIR, 1 (1), pp. 21-46; 
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},
	month = {2008///},
	pages = {739 - 749},
	address = {Povoa de Varzim},
	abstract = {In vivo observation and tracking of the Arabidopsis thaliana root meristem, by time-lapse confocal microscopy, is important to understand mechanisms like cell division and elongation. The research herein described is based on a large amount of image data, which must be analyzed to determine the location and state of cells. The automation of the process of cell detection/marking is an important step to provide research tools for the biologists in order to ease the search for special events, such as cell division. This paper discusses a hybrid approach for automatic cell segmentation, which selects the best cell candidates from a starting watershed-based image segmentation and improves the result by merging adjacent regions. The selection of individual cells is obtained using a Support Vector Machine (SVM) classifier, based on the shape and edge strength of the cells{\textquoteright} contour. The merging criterion is based on edge strength along the line that connects adjacent cells{\textquoteright} centroids. The resulting segmentation is largely pruned of badly segmented and over-segmented cells, thus facilitating the study of cell division. {\textcopyright} 2008 Springer-Verlag Berlin Heidelberg.},
	keywords = {Arabidopsis (Arabidopsis thaliana), Arabidopsis thaliana (ELIP), Cell divisions, Cell image segmentation, cell proliferation, Cell segmentation, Cells, confocal microscopy, Digital image storage, Heidelberg (CO), Hybrid approaches, Image analysis, Image data, image processing, Image segmentation, Imaging techniques, In vivo observation, Individual (PSS 544-7), International conferences, Merging, Microscopic examination, Research tools, Root meristem, Strength (IGC: D5/D6), Support Vector Machine (SVM), Support vector machines, Time lapse confocal microscopy},
	isbn = {03029743 (ISSN); 3540698116 (ISBN); 9783540698111 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-47749134578\&partnerID=40\&md5=3b05fc0d5d004216e681d9c3e2199aac},
	author = {Marcuzzo, M. and Quelhas, P. and Campilho, A. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@book {2345,
	title = {Joint detection of the carotid boundaries in longitudinal B-mode images},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {5112 LNCS},
	year = {2008},
	note = {Conference code: 72695Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-540-69812-8_75Language of Original Document: EnglishCorrespondence Address: Rocha, R.; INEB - Instituto de Engenharia Biom{\'e}dica, Divis{\~a}o de Sinal e Imagem, Campus FEUP, 4200-465 Porto, PortugalReferences: Gee, A., Prager, R., Treece, G., Berman, L., Engineering a freehand 3D ultrasound system (2003) Pattern Recogn. Lett, 24 (4-5), pp. 757-777;Sonka, M., Liang, W., Stefancik, R., Stolpen, A., Handbook of Medical Imaging: Medical Image Processing and Analysis (2000) SPIE, 2; 
Gustavsson, T., Liang, Q., Wendelhag, I., Wikstrand, J., A dynamic programming procedure for automated ultrasonic measurement of the carotid artery (1994) Computers in Cardiology, 297-300; 
Liang, Q., Wendelhag, I., Wikstrand, J., Gustavsson, T., A multiscale dynamic programming procedure for boundary detection in ultrasound artery images (2000) IEEE Trans. Med. Imag, 19 (2), pp. 127-142; 
Cheng, D., Jiang, X., Schmidt-Trucksass, A., Cheng, K., Automatic intima-media thickness measurement of carotid artery wall in B-mode sonographic images (2006) IEEE International Symposium on Biomedical Imaging (ISBI, pp. 912-915. , IEEE, Los Alamitos; 
Halenka, M., (1999) Noninvasive measurement of early atherosclerosis by high-resolution B-mode ultrasonography, 142, pp. 7-12. , Acta- Universitatis Palackianae Olomucensis Facultatis Medicae; 
Yu, Y., Acton, S., Speckle reducing anisotropic diffusion (2002) IEEE Trans. Image Processing, 11 (11), pp. 1260-1270; 
Yu, Y., Acton, S., Edge detection in ultrasound imagery using the instantaneous coefficient of variation (2004) IEEE Trans. Image Processing, 13 (12), pp. 1640-1655; 
Canny, J., A computational approach to edge detection (1986) IEEE Trans. Pattern Analysis and Machine Intelligence, 8 (6), pp. 679-698; 
Guo, Z., Hall, R.W., Parallel thinning with two-subiteration algorithms (1989) Commun. ACM, 32 (3), pp. 359-373; 
Black, M.J., Sapiro, G., Marimont, D.H., Heeger, D., Robust anisotropic diffusion (1998) IEEE Trans. Image Processing, 7 (3), pp. 421-432; 
Tauber, C., (2005) Filtrage anisotrope robuste et segmentation par B-spline snake: Application aux images {\'e}chographiques, , PhD thesis, Institut National Polytechnique de Toulouse, Toulouse F{\'e}vrier; 
de Boor, C., (1978) A Practical Guide to Splines, , Springer, Heidelberg
},
	month = {2008///},
	pages = {760 - 769},
	address = {Povoa de Varzim},
	abstract = {This paper proposes a new approach for the automatic segmentation of the carotid adventitia in longitudinal B-scans, with and without the presence of plaque. The top and bottom adventitia contours are jointly detected with a 3D dynamic programing scheme that searches for the best pair of boundaries according to a specified fuzzy cost function. Some discriminating features of the adventitia in B-mode images are used to reduce the attraction by other edges. The final contours are filtered with a smoothing spline fitting. The proposed approach was quantitatively evaluated in a set of 38 images. In order to avoid high correlation of the results, a maximum of two images was selected from each patient. The carotid boundaries manually traced by a medical expert were used as the ground truth. Several statistics show that the proposed algorithm gives good results in most of the cases, including many poor quality images. Examples of the detected contours are presented and compared with the ground truth. {\textcopyright} 2008 Springer-Verlag Berlin Heidelberg.},
	keywords = {Automatic segmentations, B-Mode imaging, Edge detection, Fuzzy costs, Ground truth, Heidelberg (CO), Image analysis, Image enhancement, Imaging techniques, International conferences, Joint detection (JD), Medical experts, New approaches, Regression analysis, Smoothing splines},
	isbn = {03029743 (ISSN); 3540698116 (ISBN); 9783540698111 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-47749116940\&partnerID=40\&md5=05a049b90326c67d79da52c7a60679f7},
	author = {Rocha, R. and Campilho, A. and Silva, J.}
}
@book {2343,
	title = {Minimizing the imbalance problem in chromatographic profile classification with one-class classifiers},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {5112 LNCS},
	year = {2008},
	note = {Conference code: 72695Cited By (since 1996): 2Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-540-69812-8_41Language of Original Document: EnglishCorrespondence Address: Sousa, A. V.; Instituto de Engenharia Biom{\'e}dica, Rua Roberto Frias, 4200-465 Porto, Portugal; email: ats@isep.ipp.ptReferences: Keir, G., Winchester, B.G., Clayton, P., Carbohydrate-deficient Glycoprotein Syndromes: Inborn Errors of Protein Glycosylate (1999) Ann. Clin. Biochem, 36 (PART 1), pp. 20-36;Durand, G., Seta, N., Protein Glycosylation and Diseases: Blood and Urinary Oligosaccharides as Markers for Diagnosis and Therapeutic Monitoring (2000) Clin. Chem, 46, pp. 795-805; 
Scriver, S.R., Beaudet, A.L., Sly, W.S., Valle, D., (2001) The Metabolic Bases of Inherited Disease, 3. , MCGraw-Hill, New-York; 
Friedman, R.B., Williams, M.A., Moser, H.W., Kolodny, E.H., Improved Thin-Layer Chromatographic Method in the Diagnosis of Mannosidosis (1978) Clinical Chemistry, 24, pp. 1576-1577; 
Kurczynski, T.W., Kendzierski, K.S., Sewell, A.C., Kuczynski, T.W., Urinary Oligosaccharides in Pregnant or Lactating Women: Pitfall in Screening (1993) Clin. Chem, 39, pp. 2346-2347; 
Sewell, A.C., Urinary Oligosaccharides. Techniques in Diagnostic Human (1991) Biochemical Genetics, 219-231; 
Duvdevani-Bar, S., Edelman, S., Visual Recognition and Categorization on the Basis of Similarities to Multiple Class Prototypes (1999) Int. J. Comput. Vision, 33, pp. 201-228; 
Pekalska, E., Duin, R.P.W., Dissimilarity Representations Allow for Building Good Classifiers (2002) Pattern Recogn. Lett, 23, pp. 943-956; 
Goldstone, R.L., Son, J.Y., Similarity (2005) Cambridge Handbook of Thinking and Reasoning, pp. 13-36. , Holyoak, K, Morrison, R, eds, Cambridge University Press, Cambridge; 
Pekalska, E., Duin, R.P.W., (2005) The Dissimilarity Representation for Pattern Recognition -Fundations and Applications, 64. , World Scientific Publishing, Singapore; 
Lavine, B.K., Clustering and Classification of Analytical Data (2000) Encyclopedia of Analytical Chemistry: Instrumentation and Applications, pp. 9689-9710. , Meyers, R.A, ed, John Wiley \& Sons, Chichester; 
Kubat, M., Holte, R., Matwin, S.: Learning when Negative Examples Abound. In: van Someren, M., Widmer, G. (eds.) ECML 1997. LNCS, 1224, pp. 146-153. Springer, Heidelberg (1997)Skurichina, M., Raudys, S., Duin, R.P.W., K-Nearest Neighbors Directed Noise Injection in Multilayer Perceptron Training (2000) IEEE Transactions on Neural Networks, 11; 
Chawla, N.V., Japkowicz, N., Kolcz, A., Editorial: Special Issue: Learning from Imbalanced Data Sets (2004) Sigkdd Explorations, 6, pp. 1-6; 
Tax, D.M.J., (2001) One-class Classification; Concept-learning in the Absence of Counter-examples, , Ph.D. thesis: Delft University of Technology; 
Sousa, A.V., Mendon{\c c}a, A.M., Campilho, A., Chromatographic Pattern Classification (2007) IEEE Trans. on Biomedical Engineering, , doi:10.1109/TBME.2008.919852; 
Paclik, P., Duin, R.P.W., Dissimilarity-based Classification of Spectra: Computational Issues (2003) Real-Time Imaging, 9, pp. 237-244; 
Paclik, P., Duin, R.P.W., Classifying Spectral Data using Relational Representation (2003) Proc. of Spectral Imaging Workshop, , Graz, Austria; 
Sousa, A.V., Mendon{\c c}a, A.M., Campilho, A., Dissimilarity-based Classification of Chromatographic Profiles (2007) Pattern Analysis and Applications, , doi:10.1007/sl0044-008-0113-2; 
Pekalska, E., Paclik, P., Duin, R.P.W., A Generalized Kernel Approach to Dissimilarity-based Classifcation (2001) Journal of Machine Learning Research, 2, pp. 175-211; 
Ripley, B.D., (2004) Pattern Recognition and Neural Networks, , Cambridge University Press, Cambridge; 
Visa, S., Ralescu, A., Learning Imbalanced and Overlapping Classes Using Fuzzy Sets (2003) Workshop on Learning from Imbalanced Datasets II, ICML, , Washington DC
},
	month = {2008///},
	pages = {413 - 422},
	address = {Povoa de Varzim},
	abstract = {This paper presents a new classification approach to deal with class imbalance in TLC patterns, which is due to the huge difference between the number of normal and pathological cases as a consequence of the rarity of LSD diseases. The proposed architecture is formed by two decision stages: the first is implemented by a one-class classifier aiming at recognizing most of the normal samples; the second stage is a hierarchical classifier which deals with the remaining outliers that are expected to contain the pathological cases and a small percentage of normal samples. We have also evaluated this architecture by a forest of classifiers, using the majority voting as a rule to generate the final classification. The results that were obtained proved that this approach is able to overcome some of the difficulties associated with class imbalance. {\textcopyright} 2008 Springer-Verlag Berlin Heidelberg.},
	keywords = {Class imbalances, Classification (of information), Classification approach, Classifiers, Heidelberg (CO), Hierarchical classifiers, Image analysis, Imaging techniques, Imbalance problems, International conferences, Learning systems, Majority voting, Pathological cases},
	isbn = {03029743 (ISSN); 3540698116 (ISBN); 9783540698111 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-47849088119\&partnerID=40\&md5=df911723c51c6f540287fdd675267725},
	author = {Sousa, A.V. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@book {2344,
	title = {Region and graph-based motion segmentation},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {5112 LNCS},
	year = {2008},
	note = {Conference code: 72695Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-540-69812-8_60Language of Original Document: EnglishCorrespondence Address: Monteiro, F. C.; INEB - Instituto de Engenharia Biom{\'e}dicaPortugalReferences: Ayer, S., Sawhney, U.S.: Layered representation of motion video using robust maximum-likelihood estimation of mixture models and mdl encoding. In: Proc. IEEE International Conference on Computer Vision, June 1995, pp. 777-784 (1995)Brox, T., (2005) From pixels to regions: Partial differential equation in image analysis, , PhD thesis, Department of Mathematics and Computer Science, Saarland University, Germany;Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High Accuracy Optical Flow Estimation Based on a, Theory for Warping. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, 3024, pp. 25-36. Springer, Heidelberg (2004)Bruhn, A., Weickert, J., Schn{\"o}rr, C., Lucas/Kanade meets Horn/ Schunck: Combining local and global optic flow methods (2005) International Journal of Computer Vision, 61 (3), pp. 1-21; 
Chang, S.-F., Sikora, T., Puri, A., Overview of the MPEG-7 standard (2001) IEEE Transactions on Circuits and Systems for Video Technology, 11 (6), pp. 688-695; 
Horn, B.K.P., Schunck, B.G., Determining optical flow (1981) Artificial Intelligence, 17 (1-3), pp. 185-203; 
Monteiro, F.C., (2007) Region-based spatial and temporal image segmentation, , PhD thesis, Faculdade de Engenharia da Universidade do Porto, Portugal; 
Monteiro, F.C., Campilho, A.: Spectral Methods in Image Segmentation: A Combined Approach. In: Marques, J.S., P{\'e}rez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, 3523, pp. 191-198. Springer, Heidelberg (2005)MPEG4. MPEG-4 video verification model, version 15.0. ISO/IEC/JTC1/SC29/WG11 N3093 1999Papenberg, N., Bruhn, A., Brox, T., Didas, S., Weickert, J., Highly accurate optic flow computation with theoretically justified warping (2006) Int. Journal of Computer Vision, 67 (2), pp. 141-158; 
Shi, J., Malik, J., Motion segmentation and tracking using normalized cuts (1998) Proc. of IEEE Int. Conference on Computer Vision, pp. 1154-1160; 
Smith, P., (2001) Edge-based motion segmentation, , PhD thesis, Department of Engineering, University of Cambridge; 
Vasconcelos, N., Lippman, A., Empirical bayesian motion segmentation (2001) IEEE Transactions on Pattern Analysis and Machine Intelligence, 23 (2), pp. 217-221; 
Wang, J., Adelson, E., Representing moving images with layers (1994) IEEE Transactions on Image Processing, 3 (5), pp. 625-638; 
Weiss, Y., Adelson, E.H., A unified mixture framework for motion segmentation: Incorporating spatial coherence and estimating the number of models (1996) Proc. of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 321-326. , San Francisco, USA, June
},
	month = {2008///},
	pages = {609 - 618},
	address = {Povoa de Varzim},
	abstract = {This paper describes an approach for integrating motion estimation and region clustering techniques with the purpose of obtaining precise multiple motion segmentation. Motivated by the good results obtained in static segmentation we propose a hybrid approach where motion segmentation is achieved within a region-based clustering approach taken the initial result of a spatial pre-segmentation and extended to include motion information. Motion vectors are first estimated with a multiscale variational method applied directly over the input images and then refined by incorporating segmentation results into a region-based warping scheme. The complete algorithm facilitates obtaining spatially continuous segmentation maps which are closely related to actual object boundaries. A comparative study is made with some of the best known motion segmentation algorithms. {\textcopyright} 2008 Springer-Verlag Berlin Heidelberg.},
	keywords = {Clustering approach, Comparative studies, Conformal mapping, Estimation, Flow of solids, Graph-based, Heidelberg (CO), Hybrid approaches, Image analysis, Image segmentation, Imaging techniques, Input Images, International conferences, Maps, Motion estimation, Motion information, Motion segmentation, Motion vector (MV), Multi scaling, Multiple motion segmentation, Object boundaries, Region clustering, Region-based, Segmentation maps, Segmentation results, Static segmentation, Variational methods},
	isbn = {03029743 (ISSN); 3540698116 (ISBN); 9783540698111 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-47749121099\&partnerID=40\&md5=99c29c64b01f394f71f2626a8ac6ce1a},
	author = {Monteiro, F.C. and Campilho, A.}
}
@conference {2333,
	title = {Tracking of Arabidopsis thaliana root cells in time-lapse microscopy},
	booktitle = {Proceedings - International Conference on Pattern RecognitionProc. Int. Conf. Pattern Recognit.},
	series = {2008 19th International Conference on Pattern Recognition, ICPR 2008},
	year = {2008},
	note = {Conference code: 81859Export Date: 21 November 2012Source: ScopusArt. No.: 4761746CODEN: PICRELanguage of Original Document: EnglishCorrespondence Address: Marcuzzo, M.; INEB - Instituto de Engenharia Biom{\'e}dica, Divis{\~a}o de Sinal e Imagem, Campus FEUPPortugalReferences: Beemster, G., Baskin, T., Analysis of cell division and elongation underlying the developmental acceleration of root growth in arabidopsis thaliana (1998) Plant Physiology, 116, pp. 1515-1526;Campilho, A., Garcia, B., Toorn, H., Wijk, H., Campilho, A., Scheres, B., Time-lapse analysis of stem-cell divisions in the arabidopsis thaliana root meristem (2006) The Plant Journal, 48, pp. 619-627; 
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K., Image denoising by sparse 3d transform-domain collaborative filtering (2007) IEEE Trans. on Image Processing, 16 (8), pp. 2080-2095. , August; 
Garcia, B., Campilho, A., Scheres, B., Campilho, A., Automatic tracking of arabidopsis thaliana root meristem in confocal microscopy (2004) Proc. of the Inter. Conf. on Image Analysis and Recognition, pp. 166-174. , Springer LCNS 3212; 
Harris, C., Stephens, M., A combined corner and edge detector (1988) Proc. of the 4th Alvey Vision Conf.; 
Hartley, R., Zisserman, A., (2003) Multiple View Geometry in Computer Vision, , Cambridge University Press; 
Iwamoto, A., Satoh, D., Furutani, M., Maruyama, S., Ohba, H., Sugiyama, M., Insight into the basis of root growth in arabidopsis thaliana provided by a simple mathematical model (2006) J. Plant Res., 119, pp. 85-93; 
Luc, V., Soille, P., Watersheds in digital spaces: An efficient algorithm based on immersion simulations (1991) IEEE Trans. on PAMI, 13 (6), pp. 583-598; 
Marcuzzo, M., Quelhas, P., Campilho, A., Mendon{\c c}a, A.M., Campilho, A., A hybrid approach for cell image segmentation (2008) Proc. of the ICIAR, pp. 739-749. , Springer LCNS 5112; 
Meyer, F., Beucher, S., Morphological segmentation (1990) JVCIR, 1 (1), pp. 21-46. , Sept; 
Mohinder, S., Angus, P., (1993) Kalman Filtering Theory and Practice, , Prentice Hall; 
Roberts, T., Mckenna, S., Wuyts, N., Valentine, T., Bengough, A., Performance of low-level motion estimation methods for confocal microscopy of plant cells in vivo (2007) Motion07, pp. 13-13
},
	month = {2008///},
	address = {Tampa, FL},
	abstract = {In vivo observation of cells in the Arabidopsis thaliana root, by time-lapse confocal microscopy, is central to biology research. The research herein described is based on large amount of image data, which must be analyzed to determine the location and state of individual cells. Automating the process of cell tracking is an important step to create tools which will facilitate the analysis of cells{\textquoteright} evolution through time. Here we introduce a confocal tracking system designed in two stages. At the image acquisition stage, we track the area under analysis based on point-to-point correspondences and motion estimation. After image acquisition, we compute cell-to-cell correspondences through time. The final result is a temporal structured information about each cell. {\textcopyright} 2008 IEEE.},
	keywords = {Arabidopsis thaliana, Bioinformatics, Cell tracking, confocal microscopy, Image acquisition, Image data, In-vivo, Individual cells, Motion estimation, Pattern recognition, Point correspondence, Root cells, Structured information, Time-lapse microscopy, Tracking system, Two stage},
	isbn = {10514651 (ISSN); 9781424421756 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77957963663\&partnerID=40\&md5=9794c92c760039ecd3cab6b8a89fa8e3},
	author = {Marcuzzo, M. and Quelhas, P. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@conference {2327,
	title = {Watershed framework to region-based image segmentation},
	booktitle = {Proceedings - International Conference on Pattern RecognitionProc. Int. Conf. Pattern Recognit.},
	series = {2008 19th International Conference on Pattern Recognition, ICPR 2008},
	year = {2008},
	note = {Conference code: 81859Cited By (since 1996): 1Export Date: 21 November 2012Source: ScopusArt. No.: 4761587CODEN: PICRELanguage of Original Document: EnglishCorrespondence Address: Monteiro, F. C.; INEB - Instituto de Engenharia Biom{\'e}dicaPortugal; email: monteiro@ipb.ptReferences: Callaghan, R.O., Bull, D., Combined morphologicalspectral unsupervised image segmentation (2005) IEEE Transactions on Image Processing, 14 (1), pp. 49-62;Comaniciu, D., Meer, P., Mean shift: A robust approach toward feature space analysis (2002) IEEE Trans. Pattern Analysis and Machine Intelligence, 24 (5), pp. 603-619; 
Cour, T., Benezit, F., Shi, J., Spectral segmentation with multiscale graph decomposition (2005) Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition, 2, pp. 1124-1131. , June; 
De Bock, J., De Smet, P., Philips, W., Image segmentation using watersheds and normalized cuts (2005) Proceedings of SPIE - The International Society for Optical Engineering, 5675, pp. 164-173. , Imaging - Vision Geometry XIII; 
Deng, Y., Manjunath, B., Unsupervised segmentation of color-texture regions in images and video (2001) IEEE Transactions on Pattern Analysis and Machine Intelligence, 23 (8), pp. 800-810; 
Martin, D., Fowlkes, C., Tal, D., Malik, J., A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics (2001) Proc. of IEEE Int. Conference on Computer Vision, 2, pp. 416-423. , http://www.cs.berkeley.edu/projects/vision/grouping/segbench/, online at; 
Monteiro, F., (2007) Region-based Spatial and Temporal Image Segmentation, , PhD thesis, Faculdade de Engenharia da Universidade do Porto, Portugal
},
	month = {2008///},
	address = {Tampa, FL},
	abstract = {This paper proposes a new framework to image segmentation which combines edge- and region-based information with spectral techniques through the morphological algorithm of watersheds. A pre-processing step is used to reduce the spatial resolution without losing important image information. An initial partitioning of the image into primitive regions is set by applying a rainfalling watershed algorithm on the image gradient magnitude. This initial partition is the input to a computationally efficient region segmentation process which produces the final segmentation. The latter process uses a region-based similarity graph representation of the image regions. The experimental results clearly demonstrate the effectiveness of the proposed approach to produce simpler segmentations and to compare favourably with state-of-the-art methods. {\textcopyright} 2008 IEEE.},
	keywords = {Computationally efficient, Edge detection, Graph representation, Image gradients, Image information, Image regions, Image segmentation, Landforms, Pre-processing step, Process use, Rainfalling watershed, Region segmentation, Region-based, Spatial resolution, Spectral techniques, State-of-the-art methods, Watersheds},
	isbn = {10514651 (ISSN); 9781424421756 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77957964931\&partnerID=40\&md5=3c1ab50936b31063f75f5b6337ed3335},
	author = {Monteiro, F.C. and Campilho, A.}
}
@book {2373,
	title = {Detection of lung nodule candidates in chest radiographs},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {4478 LNCS},
	year = {2007},
	note = {Conference code: 70962Cited By (since 1996): 3Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Pereira, C.S.; INEB - Instituto de Engenharia Biom{\'e}dica, Laborat{\'o}rio de Sinal e Imagem, Campus FEUP (Faculdade de Engenharia da Universidade do Porto), Rua Roberto Frias, s/n, 4200-465 Porto, Portugal; email: cmsp@fe.up.ptReferences: Wei, J., Hagihara, Y., Shimizu, A., Kobatake, H., Optimal image feature set for detecting lung nodules on chest X-ray images (2002) CARS 2002;Keserci, B., Yoshida, H., Computerized detection of pulmonary nodules in chest radiographs based on morphological features and wavelet snake model (2002) Medical Image Analysis, 6, pp. 431-447; 
Yoshida, H., Local contralateral subtraction based on bilateral symmetry of lung for reduction of false positives in computerized detection of pulmonary nodules, Biomedical Engineering (2004) IEEE Transactions, 51, pp. 778-789; 
Suzuki, K., Shiraishi, J., Abe, H., MacMahon, H., Doi, K., False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network (2005) Academic Radiology, 12, pp. 191-201; 
Schilham, A., van Ginneken, B., Loog, M., Multi-scale Nodule Detection in Chest Radiographs (2003) LNCS, 2878, pp. 602-609. , Ellis, R.E, Peters, T.M, eds, MICCAI 2003, Springer, Heidelberg; 
Schilham, A., van Ginneken, B., Loog, M., A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database (2006) Medical Image Analysis, 10, pp. 247-258; 
Kobatake, H., Hashimoto, S., Convergence index filter for vector fields (1999) IEEE Transactions on Image Processing, 8 (8); 
Shiraishi, J., Katsuragawa, S., Kezoe, S., Matsumoto, T., Kobayashi, T., Komatsu, K., Matsui, M., Doi, K., Development of a Digital Image Database for Chest Radiographs With and Without a Lung Nodule: Receiver Operating Characteristic Analysis of Radiologists{\textquoteright} Detection of Pulmonary Nodules (2000) American Journal of Roentgenology, 174, pp. 71-74; 
Kobatake, H., Murakami, M., Adaptive filter to detect rounded convex regions: Iris filter (1996) Int. Conf. Pattern Recognition, 11, pp. 340-344; 
Wei, J., Hagihara, Y., Kobatake, H.: Detection of cancerous tumors on chest X-ray images - Candidate detection filter and its evaluation, ICIP99, no. 27AP4.2 (1999)Kobatake, H., Wei, J., Yoshinaga, Y., Hagihara, Y., Shimizo, A., Nonlinear Adaptine Convergence Index Filters and Their Characteristics (2000) Int. Conf. Pattern Recognition, pp. 522-525
},
	month = {2007///},
	pages = {170 - 177},
	address = {Girona},
	abstract = {This paper presents an automated method for the selection of a set of lung nodule candidates, which is the first stage of a computeraided diagnosis system for the detection of pulmonary nodules. An innovative operator, called sliding band filter (SBF), is used for enhancing the lung field areas. In order to reduce the influence of the blood vessels near the mediastinum, this filtered image is multiplied by a mask that assigns to each lung field point an a priori probability of belonging to a nodule. The result is further processed with a watershed segmentation method that divides each lung field into a set of non-overlapping areas. Suspicious nodule locations are associated with the regions containing the highest regional maximum values. The proposed method, whose result is an ordered set of lung nodule candidate regions, was evaluated on the 247 images of the JSRT database with very promising results. {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.},
	keywords = {Blood Vessels, Computer aided diagnosis, Database systems, Lung nodule candidates, Non-overlapping areas, Pulmonary diseases, Pulmonary nodules, Radiography, Sliding band filter (SBF)},
	isbn = {03029743 (ISSN); 9783540728481 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-38049105791\&partnerID=40\&md5=9af5e55fe146cd0455d2302e5383e14c},
	author = {Pereira, C.S. and Fernandes, H. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@book {2372,
	title = {Evaluation of contrast enhancement filters for lung nodule detection},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {4633 LNCS},
	year = {2007},
	note = {Conference code: 70855Cited By (since 1996): 8Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Pereira, C.S.; INEB - Instituto de Engenharia Biom{\'e}dica, Laborat{\'o}rio de Sinal e Imagem, Campus FEUP (Faculdade de Engenharia da Universidade do Porto), Rua Roberto Frias, s/n, 4200-465 Porto, Portugal; email: cmsp@fe.up.ptReferences: Kobatake, H., Murakami, M., Adaptive filter to detect rounded convex regions: Iris filter (1996) Int. Conf. Pattern Recognition, 2, pp. 340-344;Kobatake, H., Hashimoto, S., Convergence index filter for vector fields (1999) IEEE Transactions on Image Processing, 8 (8); 
Wei, J., Hagihara, Y., Kobatake, H.: Detection of cancerous tumors on chest X-ray images - Candidate detection filter and its evaluation. In: ICIP99, 27AP4.2 (1999)Kobatake, H., Wei, J., Yoshinaga, Y., Hagihara, Y., Shimizo, A., Nonlinear Adaptinc Convergence Index Filters and Their Characteristics (2000) Int. Conf. Pattern Recognition, pp. 522-525; 
Shiraishi, J., Katsuragawa, S., Kezoe, S., Matsumoto, T., Kobayashi, T., Komatsu, K., Matsui, M., Doi, K., Development of a Digital Image Database for Chest Radiographs With and Without a Lung Nodule: Receiver Operating Characteristic Analysis of Radiologists{\textquoteright} Detection of Pulmonary Nodules (2000) American Journal of Roentgenology, 174, pp. 71-74; 
Keserci, B., Yoshida, H., Computerized detection of pulmonary nodules in chest radiographs based on morphological features and wavelet snake model (2002) Medical Image Analysis, 6, pp. 431-447; 
Song, X., Pogue, B., Jiang, S., Doyley, M., Dehghani, H., Tosteson, T., Paulsen, K., Automated region detection based on the contrast-to-noise ratio in near-infrared tomography (2004) Applied Optics, 43 (5), pp. 1053-1062; 
Singh, S., Bovis, K., An evaluation of contrast enhancement techniques for mammographic breast masses (2005) IEEE Transactions On Information Technology in Biomedicine, 9 (1), pp. 109-119; 
Wei, J., Hagihara, Y., Shimizu, A., Kobatake, H., Optimal image feature set for detecting lung nodules on chest X-ray images (2002) CARS; 
Suzuki, K., Shiraishi, J., Abe, H., MacMahon, H., Doi, K., False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network (2005) Academic Radiology, 12, pp. 191-201; 
},
	month = {2007///},
	pages = {878 - 888},
	address = {Montreal},
	abstract = {The aim of this paper is to evaluate and compare the performance of three convergence index (CI) filters when applied to the enhancement of chest radiographs, aiming at the detection of lung nodules. One of these filters, the sliding band filter (SBF), is the proposal of an innovative operator, while the other two CI class members, the iris filter (IF) and the adaptive ring filter (ARF), are already known in this application. To demonstrate the adequacy of the new filter for the enhancement of chest x-ray images, we calculated several figures of merit with the goal of comparing (i) the contrast enhancement capability of the filters, and (ii) the behavior of the filters for the detection of lung nodules. The results obtained for 154 images with nodules of the JSRT database show that the SBF outperforms both the IF and ARF. The proposed filter demonstrated to be a promising enhancement method, thus justifying its use in the first stage of a computer-aided diagnosis system for the detection of lung nodules. {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.},
	keywords = {Adaptive systems, Contrast enhancement filters, Database systems, Image segmentation, Iris filter (IF), Medical imaging, Radiography, X rays},
	isbn = {03029743 (ISSN); 9783540742586 (ISBN)},
	author = {Pereira, C.S. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@book {2398,
	title = {The class imbalance problem in TLC image classification},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {4142 LNCS},
	year = {2006},
	note = {Conference code: 68310Cited By (since 1996): 1Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Sousa, A.V.; Instituto Superior de Engenharia do Porto, Rua Dr. Antonio Bernardino de A. 431, 4200-072 Porto, Portugal; email: ats@isep.ipp.ptReferences: Kubat, M., Holte, R., Matwin, S., Learning when negative examples abound (1997) European Conference on Machine Learning, pp. 146-153;Lazarevi{\'c}, A., Srivastava, J., Kumar, V., Data mining for analysis of rare events: A case study in security, financial and medical applications (2004) PAKDD-2004; 
Skurichina, M., Raudys, S., Duin, R.P.W., K-nearest neighbors directed noise injection in multilayer perceptron training (2000) IEEE Transactions on Neural Networks, 11 (2); 
Chawla, N.V., Japkowicz, N., Kolcz, A., Editorial: Special issue on learning from imbalanced data sets (2004) Sigkdd Explorations, 6 (1), pp. 1-6; 
Japkowicz, N., Class imbalances: Are we focusing on the right issue? Workshop on Learning from Imbalanced Datasets II, p. 6. , (2003) ICML, Washington DC; 
Chawla, N.V., Lazarevi{\'c}, A., Hall, L.O., Bowyer, K.W., SMOTEBoost: Improving the prediction of minority class in boosting (2003) 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), pp. 107-119; 
Domingos, P., MetaCost: A general method for making classifiers cost-sensitive (1999) Conference on Knowledge Discovery in Data Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 155-164; 
Fan, W., Stolfo, S., Zhang, J., Chan, P., Adacost: Misclassication cost-sensitive boosting (1999) Proceedings of Sixteenth International Conference on Machine Learning, pp. 983-990; 
Tax, D.M.J., One-class classification; Concept-learning in the absence of counterexamples (2001) ASCI Dissertation Series, 65, pp. 1-190. , PhD thesis Delft University of Technology; 
Ridder, D., Tax, D.M.J., Duin, R.P.W., An experimental comparison of one-class classification methods (1998) Proc ASCI{\textquoteright}98, 4th Annual Conf of the Advanced School for Computing and Imaging, pp. 213-218. , (Lommel, Belgium, June 9-11), ASCI, Delft; 
Lazarevic, A., Ertoz, L., Ozgur, A., Srivastava, J., Kumar, V., A comparative study of anomaly detection schemes in network intrusion detection (2003) Proceedings of Third SIAM Conference on Data Mining, , San Francisco; 
Weiss, G.M., Mining with rarity: A unifying framework (2004) Sigkdd Explorations, 6 (1), pp. 7-19; 
Lewis, D.D., Gale, W.A., A sequential algorithm for training text classifiers (1994) Proceedings of International ACM Conference on Research and Development in Information Retrieval, pp. 3-12; 
Visa, S., Ralescu, A., Learning imbalanced and overlapping classes using fuzzy sets (2003) Workshop on Learning from Imbalanced Datasets II, , ICML, Washington DC; 
Sousa, A.V., Aguiar, R., Mendon{\c c}a, A.M., Campilho, A., Automatic lane and band detection in images of thin layer chromatography (2004) LNCS, 3212. , Springer; 
Sousa, A.V., Mendon{\c c}a, A.M., Campilho, A., Aguiar, R., Miranda, C.S., Feature extraction for classification of thin-layer chromatography images (2005) LNCS, 3656. , Springer; 
Japkowicz, N., Learning from imbalanced data sets: A comparison of various strategies (2000) Proceedings of Learning from Imbalanced Data, pp. 10-15; 
Kubat, M., Holte, R., Matwin, S., Machine learning for the detection of oil spills in satellite radar images (1998) Machine Learning, 30, pp. 195-215; 
Ha, T., Bunke, H., Off-Line, handwritten numeral recognition by perturbation method (1997) Pattern Analysis and Machine Intelligence, 19 (5), pp. 535-539; 
Lee, S., Noisy replication in skewed binary classification (2000) Computational Statistics \& Data Analysis; 
Heijden, F., Robert, P.W.D., Ridder, D., Tax, D.M.J., (2004) Classification, Parameter Estimation and State Estimation, , Wiley; 
Demant, C., Streicher-Abel, B., Waszkewitz, P., (1999) Industrial Image Processing, , Springer-Verlag; 
Chawla, N.V., Bowyer, K., Hall, L., Kegelmeyer, W., SMOTE: Synthetic minority over-sampling technique (2002) Jounal of Artificial Intelligence Research, 16, pp. 321-357
},
	month = {2006///},
	pages = {513 - 523},
	address = {Povoa de Varzim},
	abstract = {The paper presents the methodology developed to solve the class imbalanced problem that occurs in the classification of Thin-Layer Chromatography (TLC) images. The proposed methodology is based on resampling, and consists in the undersampling of the majority class (normal class), while the minority classes, which contain Lysosomal Storage Disorders (LSD) samples, are oversampled with the generation of synthetic samples. For image classification two approaches are presented, one based on a hierarchical classifier and another uses a multiclassifier system, where both classifiers are trained and tested using balanced data sets. The results demonstrate a better performance of the multiclassifier system using the balanced sets. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
	keywords = {Chromatographic analysis, Class imbalanced problem, Classification (of information), Hierarchical systems, Lysosomal Storage Disorders (LSD), Medical imaging, Multiclassifier system, Problem solving, Thin-Layer Chromatography (TLC) images},
	isbn = {03029743 (ISSN); 3540448942 (ISBN); 9783540448945 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33749667604\&partnerID=40\&md5=3b4a717035de29f4bed59a04613836cd},
	author = {Sousa, A.V. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@book {2396,
	title = {Lung parenchyma segmentation from CT images based on material decomposition},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {4142 LNCS},
	year = {2006},
	note = {Conference code: 68310Cited By (since 1996): 1Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Vinhais, C.; Instituto de Engenharia Biom{\'e}dica, Laborat{\'o}rio de Sinal e Imagem Biom{\'e}dica, Campus da FEUP, Rua Roberto Frias, s/n, 4200-465 Porto, Portugal; email: cav@isep.ipp.ptReferences: Armato, S.G., Giger, M.L., Moran, C.J., Doi, K., MacMahon, H., Computerized detection of lung nodules in computed tomography scans (1998) Proceedings of the 1st International Workshop on Computer-aided Diagnosis, pp. 119-124;Armato III, S.G., Giger, M.L., Morgan, C.J., Blackburn, J.T., Doi, K., MacMahon, H., Computerized detection of pulmonary nodules on CT scans (1999) RadioGraphics, 19 (5), pp. 1303-1311; 
Brown, M.S., McNitt-Gray, M.F., Goldin, J.G., Suh, R.D., Sayre, J.W., Aberle, D.R., Patient-specific models for lung nodule detection and surveillance in CT images (2001) IEEE Trans. on Medical Imaging, 20 (12), pp. 1242-1250; 
Gurcan, M.N., Sahiner, B., Petrick, N., Chan, H.-P., Kazerooni, E.A., Cascade, P.N., Hadjiiski, L., Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system (2002) Medical Physics, 29 (11), pp. 2552-2558; 
Armato III, S.G., Sensakovic, W.F., Automated lung segmentation for thoracic CT: Impact on computer-aided diagnosis (2004) Academic Radiology, 11, pp. 1011-1021; 
Kemerink, G.J., Lamers, R.J.S., Pellis, B.J., Kruize, H.H., Van Engelshoven, J.M.A., On segmentation of lung parenchyma in quantitative computed tomography of the lung (1998) Medical Physics, 25 (12), pp. 2432-2439; 
Brown, M.S., McNitt-Gray, M.F., Goldin, J.G., Greaser, L.E., Aberle, D.R., Knowledge-based method for segmentation and quantitative analysis of lung function from CT (1998) Proceedings of the 1st International Workshop on Computer-aided Diagnosis, pp. 113-118; 
Leader, J.K., Zheng, B., Rogers, R.M., Sciurba, F.C., Perez, A., Chapman, B.E., Patel, S., Gur, D., Automated lung segmentation in X-ray computed tomography: Development and evaluation of a heuristic threshold-based scheme (2003) Academic Radiology, 10, pp. 1224-1236; 
Hu, S., Hoffman, E.A., Reinhardt, J.M., Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images (2001) IEEE Trans. on Medical Imaging, 20 (6), pp. 490-498; 
Alvarez, R.E., Macovsky, A., Energy-selective reconstruction in X-ray computerized tomography (1976) Phys. Med. Biol., 21, pp. 733-744; 
Lehmann, L.A., Alvarez, R.E., Macovski, A., Brody, W.R., Generalized image combinations in dual KVP digital radiography (1981) Medical Physics, 8 (5), pp. 659-667; 
Macovsky, A., (1983) Medical Imaging Systems, , Information and Systems Science Series, Prentice Hall, Inc; 
Tissue substitutes in radiation dosimetry and measurement (1989) Report 44 of the International Commission on Radiation Units and Measurements, , Bethesda, MD; 
Berger, M.J., Hubbell, J.H., XCOM: Photon cross sections on a personal computer (1987) NBSIR, 87 (3597); 
Boone, J.M., Seibert, J.A., An accurate method for computer-generating tungsten anode x-ray spectra from 30 to 140 kV (1997) Medical Physics, 24 (11), pp. 1661-1670; 
Rijsbergen, C.J.V., (1979) Information Retrieval, , London, Butterworths, 2nd ed
},
	month = {2006///},
	pages = {624 - 635},
	address = {Povoa de Varzim},
	abstract = {We present a fully automated method for extracting the lung region from volumetric X-ray CT images based on material decomposition. By modeling the human thorax as a composition of different materials, the proposed method follows a threshold-based, hierarchical voxel classification strategy. The segmentation procedure involves the automatic computation of threshold values and consists on three main steps: patient segmentation and decomposition, large airways extraction and lung parenchyma decomposition, and lung region of interest segmentation. Experimental results were performed on thoracic CT images acquired from 30 patients. The method provides a reproducible set of thresholds for accurate extraction of the lung parenchyma, needed for computer aided diagnosis systems. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
	keywords = {Biological organs, Classification (of information), Computer simulation, Computerized tomography, Image segmentation, Lung parenchyma, Medical imaging, Patient segmentation, Patient treatment, Volumetric X-ray CT images, Voxel classification strategy},
	isbn = {03029743 (ISSN); 3540448942 (ISBN); 9783540448945 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33749660395\&partnerID=40\&md5=d07d50c5605d8c249a87771d3469fa78},
	author = {Vinhais, C. and Campilho, A.}
}
@book {2397,
	title = {A multiclassifier approach for lung nodule classification},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {4142 LNCS},
	year = {2006},
	note = {Conference code: 68310Cited By (since 1996): 3Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Pereira, C.S.; Instituto de Engenharia Biom{\'e}dica, Rua Roberto Frias, 4200-465 Porto, Portugal; email: cpereira@estgl.ipv.ptReferences: Jemal, A., Murray, T., Ward, E., Samuels, A., Tiwari, R.C., Ghafoor, A., Feuer, E.J., Thun, M.J., (2005) Cancer Statistics, 2005, 55, pp. 10-30. , CA Cancer J Clin;Keserci, B., Yoshida, H., Computerized detection of pulmonary nodules in chest radiographs based on morphological features and wavelet snake model (2002) Medical Image Analysis, 6, pp. 431-447; 
Van Ginneken, B., Romeny, B.M.H., Viergever, M.A., Computer-aided diagnosis in chest radiography: A survey (2001) IEEE Transactions on Medical Imaging, 20 (12), pp. 1228-1241; 
Wei, J., Hagihara, Y., Shimizu, A., Kobatake, H., Optimal image feature set for detecting lung nodules on chest X-ray images (2002) CARS 2002; 
Van Ginneken, B., Katsuragawa, S., Ter Haar Romeny, B.M., Kunio, D., Viergever, M.A., Automatic detection of abnormalities in chest radiographs using local texture analysis (2002) IEEE Transactions on Medical Imaging, 21, pp. 139-149; 
Schilham, A.M.R., Van Ginneken, B., Loog, M., Multi-scale nodule detection in chest radiographs (2003) Lecture Notes in Computer Science, LNCS 2878, pp. 602-609; 
Shiraishi, J., Katsuragawa, S., Ikezoe, J., Matsumoto, T., Kobayashi, T., Matsu, K.-I.K., Matsui, M., Doi, K., Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristic analysis of radiologists{\textquoteright} detection of pulmonary nodules (2000) American Journal of Roentgenology, 174, pp. 71-74; 
Yoshida, H., Local contralateral subtraction based on bilateral symmetry of lung for reduction of false positives in computerized detection of pulmonary nodules (2004) IEEE Transactions on Biomedical Engineering, 51, pp. 778-789; 
Suzuki, K., Shiraishi, J., Abe, H., MacMahon, H., Doi, K., False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network (2005) Academic Radiology, 12, pp. 191-201; 
Randen, T., Husoy, J.H., Filtering for texture classification: A comparative study (1999) IEEE Transactions on Pattern Analysis and Machine Intelligence, 21 (4); 
Grigorescu, S.E., Petkov, N., Kruizinga, P., Comparison of texture features based on Gabor filters (2002) IEEE Transactions on Image Processing, 11 (10), pp. 195-203; 
Kumar, A., Pang, G.K.H., Defect detection in textured materials using Gabor filters (2002) IEEE Transactions on Industry Applications, 38 (2); 
Varma, M., Zisserman, A., Unifying statistical texture classification frameworks (2004) Image and Vision Computing, 22, pp. 1175-1183; 
Leung, T., Malik, J., Representing and recognizing the visual appearance of materials using three-dimensional textons (2001) International Journal of Computer Vision, 43, pp. 29-44; 
Alexandre, L.A., Campilho, A.C., Kamel, M., On combining classifiers using sum and product rules (2001) Pattern Recognition Letters, 22, pp. 1283-1289
},
	month = {2006///},
	pages = {612 - 623},
	address = {Povoa de Varzim},
	abstract = {The aim of this paper is to examine a multiclassifier approach to the classification of the lung nodules in X-ray chest radiographs. The approach investigated here is based on an image region-based classification whose output is the information of the presence or absence of a nodule in an image region. The classification was made, essentially, in two steps: firstly, a set of rotation invariant features was extracted from the responses of a multi-scale and multi-orientation filter bank; secondly, different classifiers (multi-layer perceptrons) are designed using different features sets and trained in different data. These classifiers are further combined in order to improve the classification performance. The obtained results are promising and can be used for reducing the false-positives nodules detected in a computer-aided diagnosis system. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
	keywords = {Biological organs, Classification (of information), Computer aided diagnosis, False-positives nodules, Medical imaging, Multi-orientation filter bank, Radiography, X rays, X-ray chest radiographs},
	isbn = {03029743 (ISSN); 3540448942 (ISBN); 9783540448945 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33749658606\&partnerID=40\&md5=f6e83e868f5b1ee08b400ee9191138cd},
	author = {Pereira, C.S. and Alexandre, L.A. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@article {2393,
	title = {Time-lapse analysis of stem-cell divisions in the Arabidopsis thaliana root meristem},
	journal = {Plant JournalPlant J.},
	volume = {48},
	year = {2006},
	note = {Cited By (since 1996): 33Export Date: 21 November 2012Source: ScopusCODEN: PLJUEdoi: 10.1111/j.1365-313X.2006.02892.xPubMed ID: 17087761Language of Original Document: EnglishCorrespondence Address: Scheres, B.; Department of Molecular Genetics, University of Utrecht, Utrecht, Netherlands; email: b.scheres@bio.uu.nlReferences: Aida, M., Beis, D., Heidstra, R., Willemsen, V., Blilou, I., Galinha, C., Nussaume, L., Scheres, B., The PLETHORA genes mediate patterning of the Arabidopsis root stem cell niche (2004) Cell, 119, pp. 109-120;Baskin, T.I., On the constancy of cell-division rate in the root meristem (2000) Plant Mol. Biol., 43, pp. 545-554; 
Van Den Berg, C., Willemsen, V., Hendriks, G., Weisbeek, P., Scheres, B., Short-range control of cell differentiation in the Arabidopsis root meristem (1997) Nature, 390, pp. 287-289; 
Van Den Berg, C., Hage, W., Weisbeek, P., Scheres, B., Laser ablation in Arabidopsis roots: A tool to study cell-to-cell communication (1998) NATO ASI Ser., H104, pp. 237-250; 
Boisnard-Lorig, C., Colon-Carmona, A., Bauch, M., Hodge, S., Doerner, P., Bancharel, E., Dumas, C., Berger, F., Dynamic analyses of the expression of the HISTONE::YFP fusion protein in Arabidopsis show that syncytial endosperm is divided in mitotic domains (2001) Plant Cell, 13, pp. 495-509; 
Buvat, R., Structure, evolution et functionnement du m{\'e}rist{\`e}m apical de quelques dicotyl{\'e}dones (1952) Ann. Sci. Nat. Bot. Ser., 13, pp. 199-300; 
Clough, S.J., Bent, A.F., Floral dip: A simplified method for Agrobacterium-mediated transformation of Arabidopsis thaliana (1998) Plant J., 16, pp. 735-743; 
Clowes, F.A.L., Nucleic acids in root apical meristems of Zea (1956) New Phytol., 55, pp. 29-34; 
Cutler, S.R., Ehrhardt, D.W., Griffitts, J.S., Somerville, C.R., Random GFP::cDNA fusions enable visualization of subcellular structures in cells of Arabidopsis at a high frequency (2000) Proc. Natl Acad. Sci. USA, 97, pp. 3718-3723; 
Dolan, L., Janmaat, K., Willemsen, V., Linstead, P., Poethig, S., Roberts, K., Scheres, B., Cellular organization of the Arabidopsis thaliana root (1993) Development, 119, pp. 71-84; 
Dolan, L., Duckett, C.M., Grierson, C., Linstead, P., Schneider, K., Lawson, E., Dean, C., Roberts, K., Clonal relationships and cell patterning in the root epidermis of Arabidopsis (1994) Development, 120, pp. 2465-2474; 
Dolznig, H., Grebien, F., Sauer, T., Beug, H., Mullner, E.W., Evidence for a size-sensing mechanism in animal cells (2004) Nat. Cell Biol., 6, pp. 899-905; 
Furner, I.J., Pumfrey, J.E., Cell fate in the shoot apical meristem of Arabidopsis thaliana (1992) Development, 115, pp. 755-764; 
Garcia, B., Campilho, A., Scheres, B., Campilho, A., Automatic tracking of Arabidopsis thaliana root meristem in confocal microscopy (2004) ICIAR 2004 LNCS 3212, pp. 166-174. , (Campilho, A. and Kamel, M., eds). New York, Springer Verlag; 
Irish, V.F., Sussex, I.M., A fate map of the Arabidopsis embryonic shoot apical meristem (1992) Development, 115, pp. 745-753; 
Jorgensen, P., Nishikawa, J., Breitkreutz, B.J., Tyers, M., Systematic identification of pathways that couple cell growth and division in yeast (2002) Science, 297, pp. 395-400; 
Nance, J., Priess, J.R., Cell polarity and gastrulation in C. elegans (2002) Development, 129, pp. 387-397; 
O{\textquoteright}Connell, K.F., Leys, C.M., White, J.G., A genetic screen for temperature-sensitive cell-division mutants of Caenorhabditis elegans (1998) Genetics, 149, pp. 1303-1321; 
Otsu, N., A threshold selection method from gray level histograms (1979) IEEE Trans. Syst. Man. Cybernet., 9, pp. 62-66; 
Perona, P., Malik, J., Scale space and edge detection using anisotropic diffusion (1990) IEEE Trans. Pattern Anal. Mach. Intel., 12, pp. 629-639; 
Reddy, G.V., Heisler, M.G., Ehrhardt, D.W., Meyerowitz, E.M., Real-time lineage analysis reveals oriented cell divisions associated with morphogenesis at the shoot apex of Arabidopsis thaliana (2004) Development, 131, pp. 4225-4237; 
Sabatini, S., Heidstra, R., Wildwater, M., Scheres, B., SCARECROW is involved in positioning the stem cell niche in the Arabidopsis root meristem (2003) Genes Dev., 17, pp. 354-358; 
Scheres, B., Wolkenfelt, H., Willemsen, V., Terlouw, M., Lawson, E., Dean, C., Weisbeek, P., Embryonic origin of the Arabidopsis primary root and root meristem initials (1994) Development, 120, pp. 2475-2487; 
Sulston, J.E., Schierenberg, E., White, J.G., Thomson, J.N., The embryonic cell lineage of the nematode Caenorhabditis elegans (1983) Dev. Biol., 100, pp. 64-119; 
Thomas, C., Devries, P., Hardin, J., White, J., Four-dimensional imaging: Computer visualization of 3D movements in living specimens (1996) Science, 273, pp. 603-607; 
Watt, F., Hogan, B., Out of Eden: Stem cells and their niches (2000) Science, 287, pp. 1427-1430; 
Wildwater, M., Campilho, A., Perez-Perez, J.M., Heidstra, R., Blilou, I., Korthout, H., Chatterjee, J., Scheres, B., The RETINOBLASTOMA-RELATED gene regulates stem cell maintenance in Arabidopsis (2005) Cell, 123, pp. 1337-1349
},
	month = {2006///},
	pages = {619 - 627},
	abstract = {In the Arabidopsis root, asymmetric stem-cell divisions produce daughters that form the different root cell types. Here we report the establishment of a confocal tracking system that allows the analysis of numbers and orientations of cell divisions in root stem cells. The system provides direct evidence that stem cells have lower division rates than cells in the proximal meristem. It also allows tracking of cell division timing, which we have used to analyse the synchronization of root cap divisions. Finally, it gives new insights into lateral root cap formation: epidermal stem-cell daughters can rotate the orientation of the division plane like the stem cell. {\textcopyright} 2006 The Authors.},
	keywords = {Arabidopsis, Arabidopsis thaliana, article, cell cycle, Cell Division, Cells, cytology, Development, Growth, Growth kinetics, Image analysis, meristem, Photoperiod, photoperiodicity, Plant, plant root, Plant roots, Plants, Plants (botany), Real-time, stem cell, Stem cells, Stem-cell daughters, time, Time Factors},
	isbn = {09607412 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33750557662\&partnerID=40\&md5=84fa447f2b750bbd4f5de3d4d7978a4d},
	author = {Campilho, A. and Garcia, B. and Toorn, H.V.D. and Wijk, H.V. and Campilho, A. and Scheres, B.}
}
@book {2439,
	title = {Feature extraction for classification of thin-layer chromatography images},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {3656 LNCS},
	year = {2005},
	note = {Conference code: 67124Cited By (since 1996): 2Export Date: 21 November 2012Source: Scopusdoi: 10.1007/11559573_118Language of Original Document: EnglishCorrespondence Address: Sousa, A.V.; Instituto de Engenharia Biom{\'e}dica, Rua Roberto Frias, 4200-465 Porto, Portugal; email: ats@isep.ipp.ptReferences: Bajla, I., Holl{\"a}nder, I., Burg, K., Improvement of electrophoretic gel image analysis (2001) M. S.R, 1 (1);Ye, X., Suen, C., Cheriet, M., Wang, E., A recent development in image analysis of electrophoresis gels (1999) Vision Interface 99, , Canada; 
Shadle, S., Allen, D., Guo, H., Pogozelski, W., Bashkin, J., Tullius, T., Quantitative analysis of electrophoresis data: Novel curve fitting methodology and its application to the determination of aprotein-DNA binding constant (1997) Nucleic Acids Research, 25 (4); 
Sousa, A., Aguiar, R., Mendon{\c c}a, A.M., Campilho, A., Automatic lane and band detection in images of thin layer chromatography (2004) Springer Lecture Notes on Computer Science, LNCS, 3212, pp. 372-380; 
Heijden, F., Duin, R., Ridder, D., Tax, D., (2004) Classification, Parameter Estimation and State Estimation, , John Wiley and Sons, England; 
Sewell, A., (1991) Urinary Oligosaccharides. Techniques in Diagnostic Human Biochemical Genetics, pp. 219-231. , Willey-Liss, Inc; 
Thomas, G., Disorders of Glycoprotein Degradation. α-Mahnosidosis, β-Mannosidosis, Fucosidosis and Sialidosis. Disorders of Glycoprotein Degradation, , Chap. 140; 
Durant, G., Seta, N., Protein glycosylation and diseases: Blood and urinary oligosaccharides as markers for diagnosis and therapeutic monitoring (2000) Clinical Chemistry, 46 (6), pp. 795-805; 
Marques De S{\'a}, J., (2003) Applied Statistics Using SPSS, STATISTICA and MATLAD, , Springer Verlag
},
	month = {2005///},
	pages = {974 - 981},
	address = {Toronto},
	abstract = {Thin-Layer Chromatography images are used to detect and identify the presence of specific oligosaccharides, expressed by the existence, at different positions, of bands in the gel image. ID gaussian deconvolution, commonly used for band detection, does not produce good results due to the large curvature observed in the bands. To overcome this uncertainty on the band position, we propose a novel feature extraction methodology that allows an accurate modeling of curved bands. The features are used to classify the data into two different classes, to differentiate normal from pathologic cases. The paper presents the developed methodology together with the analysis and discussion of the results. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005.},
	keywords = {Edge detection, Feature extraction, Image analysis, Image segmentation, Oligosaccharides, Thin layer chromatography, Thin-Layer Chromatography images},
	isbn = {03029743 (ISSN); 3540290699 (ISBN); 9783540290698 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33645996661\&partnerID=40\&md5=6ef97f4dfec52add55258aec9e98bb52},
	author = {Sousa, A.V. and Mendon{\c c}a, A.M. and Campilho, A. and Aguiar, R. and S{\'a} Miranda, C.}
}
@book {2427,
	title = {Genetic model-based segmentation of chest X-ray images using free form deformations},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {3656 LNCS},
	year = {2005},
	note = {Conference code: 67124Export Date: 21 November 2012Source: Scopusdoi: 10.1007/11559573_116Language of Original Document: EnglishCorrespondence Address: Vinhais, C.; INEB - Instituto de Engenharia Biom{\'e}dica, Laborat{\'o}rio de Sinai e Imagem Biom{\'e}dica, Campus da FEUP, Rua Roberto Frias, s/n, 4200-465 Porto, Portugal; email: cav@isep.ipp.ptReferences: Carreira, M., Cabello, D., Penedo, M., Mosquera, A., Computer-aided diagnoses: Automatic detection of lung nodules (1998) Medical Physics, 25 (10), pp. 1998-2006;Nakaromi, N., Doi, K., MacMahon, H., Sasaki, Y., Montner, S.M., Effect on heart-size parameters computed from digital chest radiographs on detection of cardiomegaly: Potencial usefulness for computer-aided diagnosis (1991) Inv. Radiology, 26, pp. 546-550; 
Van Ginneken, B., Romeny, B.H., Viergever, M.A., Computer-aided diagnosis in chest radiography: A survey (2001) IEEE Trans. on Medical Imaging, 20 (12), pp. 1228-1241; 
Cootes, T.E., Hill, A., Taylor, C.J., Haslam, J., The use of active shape models for locating structures in medical images (1994) Image, and Vision Computing, 12 (6), pp. 355-366; 
Sederberg, T.W., Parry, S.R., Free-form deformation of solid geometric models (1986) ACM SIGGRAPH Computer Graphics, 20 (4), pp. 151-160; 
Field, D.J., Relations between the statistics of natural images and the response properties of cortical cells (1987) J. Opt. Soc. Am. A, 4 (12), pp. 2379-2394; 
Petkov, N., Kruizinga, P., Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: Bar and grating cells (1997) Biol. Cybern., 76, pp. 83-96; 
Carpenter, M.B., (1991) Core Text of Neuroanatomy. 4th Ed.; 
Lotjonen, J., Magnin, I., Nenonen, J., Katila, T., Reconstruction of 3-D geometry using 2-D profiles and a geometric prior model (1999) IEEE Trans. on Medical Imaging, 18 (10), pp. 992-1002; 
Goldberg, D.E., (1989) Genetic Algorithms in Search, Optimization and Machine Learning; 
Gofman, Y., Kiryati, N., Detecting symmetry in grey level images: The global Optimization approach (1998) International Journal of Computer Vision (IJCV), 29, pp. 29-45
},
	month = {2005///},
	pages = {958 - 965},
	address = {Toronto},
	abstract = {A method is proposed to segment digital posterior-anterior chest X-ray images. The segmentation is achieved through the registration of a deformable prior model, describing the anatomical structures of interest, to the X-ray image. The deformation of the model is performed using a deformation grid. A coarse matching of the model is done using anatomical landmarks automatically extracted from the image, and maps of oriented edges are used to guide the deformation process, optimized with a probabilistic genetic algorithm. The method is applied to extract the ribcage and delineate the mediastinum and diaphragms. The segmentation is needed for defining the lungs region, used in computer-aided diagnosis systems. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005.},
	keywords = {Computer aided analysis, Diaphragms, Feature extraction, Free Form Deformation, Gabor Filters, Genetic Algorithms, Image segmentation, Model-based Image Segmentation, Optimization, Pattern matching, X ray analysis},
	isbn = {03029743 (ISSN); 3540290699 (ISBN); 9783540290698 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33645986088\&partnerID=40\&md5=cda7c9ce6977b972158c866963cddc4b},
	author = {Vinhais, C. and Campilho, A.}
}
@book {2432,
	title = {Segmentation of ultrasonic images of the carotid},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {3656 LNCS},
	year = {2005},
	note = {Conference code: 67124Export Date: 21 November 2012Source: Scopusdoi: 10.1007/11559573_115Language of Original Document: EnglishCorrespondence Address: Rocha, R.; INEB - Instituto de Engenharia Biom{\'e}dica, Laborat{\'o}rio de Sinai e Imagem Biom{\'e}dica, Porto, PortugalReferences: Gee, A., Prager, R., Treece, G., Berman, L., Engineering a freehand 3D ultrasound system (2003) Pattern Recogn. Lett., 24, pp. 757-777;Sonka, M., Liang, W., Stefancik, R., Stolpen, A., (2000) Handbook of Medical Imaging: Medical Image Processing and Analysis, 2. , SPIE; 
Christodoulou, C.I., Pattichis, C.S., Pantziaris, M., Nicolaides, A., Texture based classification of atherosclerotic carotid plaques (2003) IEEE Trans. Med. Imaging, 22, pp. 902-912; 
Barratt, D., Ariff, B., Humphries, K., Thom, S., Hughes, A., Reconstruction and quantification of the carotid artery bifurcation from 3-d ultrasound images (2004) IEEE Trans. Med. Imaging, 23, pp. 567-583; 
Li, C., Lee, C., Minimum cross entropy thresholding (1993) Pattern Recogn, 26, pp. 617-625; 
Brink, A., Pendock, N., Minimum cross-entropy threshold selection (1996) Pattern Recogn., 29, pp. 179-188; 
Chan, T., Vese, L., Active contours without edges (2001) IEEE Trans. on Image Processing, 10, pp. 266-277; 
Vese, L.A., Chan, T.F., A multiphase level set framework for image segmentation using the Mumford and Shah model (2002) Int. J. Comput. Vision, 50, pp. 271-293; 
Touboul, P., Prati, P., Abd, V., Adrai, P.Y.S., Thibout, E., Ducimetiere, P., Use of monitoring software to improve the measurement of carotid wall thickness by B-mode imaging (1992) J. Hypertension, 10, pp. 37-41; 
Liang, Q., Wendelhag, I., Wikstrand, J., Gustavsson, T., A multiscale dynamic programming procedure for boundary detection in ultrasound artery images (2000) IEEE Trans. Med. Imaging, 19, pp. 127-142; 
Schmidt-Trucksass, A., Cheng, D., Sandrock, M., Schulte-Monting, J., Rauramaa, R., Huonker, M., Burkhardt, H., Computerized analysing system using the active contour in ultrasound measurement of carotid artery intima-media thickness (2001) Clinical Physiology, 5, pp. 561-569; 
Cheng, D., Schmidt-Trucksass, A., Cheng, K., Sandrock, M., Pu, Q., Burkhardt, H., Automatic detection of the intimal and the adventitial layers of the common carotid artery wall in ultrasound B-mode images using snakes (1999) International Conference on Image Analysis and Processing, pp. 452-457. , B. Werner; 
Cheng, D., Schmidt-Trucksass, A., Cheng, K., Burkhardt, H., Using snakes to detect the intimal and adventitial layers of the common carotid artery wall in sonographic images (2002) Computer Methods and Programs in Biomedicine, 67, pp. 27-37; 
Baillard, C., Barillot, C., Bouthemy, P., Robust adaptive segmentation of 3D medical images with level sets (2000) Technical Report, , IRISA, Campus Universitaire de Beaulieu, Prance; 
Tsai, A., Yezzi III, A.J., W., W.M., Tempany, C., Tucker, D., Fan, A., Grimson, W.E.L., Willsky, A.S., A shape-based approach to the segmentation of medical imagery using level sets (2003) IEEE Trans. Med. Imaging, 22, pp. 137-154; 
Perona, P., Malik, J., Scale-space and edge detection using anisotropic diffusion (1990) IEEE Trans. Pattern Anal. Mach. Intell., 12, pp. 629-639; 
Sethian, J.A., (1999) Level Set Methods and Fast Marching Methods, , Cambridge University Press; 
Osher, S., Fedkiw, R., (2003) Level Set Methods and Dynamic Implicit Surfaces, , Springer-Verlag. New York; 
Mumford, D., Shah, J., Optimal approximations by piecewise smooth functions and associated variational problems (1989) Communications on Pure and Applied Mathematics, 42, pp. 577-685; 
Gao, S., Bui, T.D., Image segmentation and selective smoothing by using the Mumford-Shah model (2005) IEEE Trans. on Image Processing, , To be published in
},
	month = {2005///},
	pages = {949 - 957},
	address = {Toronto},
	abstract = {A new algorithm for an effective and automatic segmentation of the carotid wall in ultrasonic images is proposed. It combines the speed of thresholding algorithms with the accuracy, flexibility and robustness of a successful geometric active contour model which incorporates an optimal image segmentation model in a level set framework. Due to the multiphase nature of these images, a sequential minimum cross entropy thresholding is used to get a first approximation of the segments, reducing the problem to a two phase segmentation. This thresholding solution is then used as a starting point for a two phase piecewise constant version of a geometric active contour model to reduce noise, smooth contours, improve their position accuracy and close eventual gaps in the carotid wall. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005.},
	keywords = {Active contour model, Algorithms, Approximation theory, Carotid wall, Image segmentation, Image segmentation model, Mathematical models, Optimization, Ultrasonic imaging},
	isbn = {03029743 (ISSN); 3540290699 (ISBN); 9783540290698 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33646014096\&partnerID=40\&md5=42380a0fabe35bd2e682b403ff68e79f},
	author = {Rocha, R. and Campilho, A. and Silva, J.}
}
@conference {2463,
	title = {Automatic delimitation of lung fields on chest radiographs},
	booktitle = {2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to NanoIEEE Int. Symp. Biomed. Imaging Macro Nano},
	series = {2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano},
	volume = {2},
	year = {2004},
	note = {Conference code: 64593Cited By (since 1996): 1Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Mendon{\c c}a, A.M.; Inst. de Engenharia Biom{\'e}dica, Lab. de Sinai e Imagem Biomedica, Campus da FEUP, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, PortugalReferences: Tsujii, O., Freedman, M., Mun, S., Automated segmentation of anatomic regions in chest radiographs using an adaptive-sized hybrid neural network (1998) Medical Physics, 25, pp. 998-1007;Armato III, S., Giger, M., MacMahon, H., Computerized detection of abnormal asymetry in digital lateral chest radiographs (1994) Medical Physics, 21, pp. 1761-1768; 
Armato III, S., Giger, M., Ashizawa, K., MacMahon, H., Automated lung segmentation in digital lateral chest radiographs (1998) Medical Physics, 25, pp. 1507-1520; 
Carrascal, F., Carreira, J., Souto, M., Automatic calculation of total lung capacity from automatically traced lung boundaries in posterior-anterior and lateral digital chest radiographs (1998) Medical Physics, 25, pp. 1118-1131; 
Ginneken, B., Romeny, B., Automatic segmentation of lung fields in chest radiographs (2000) Medical Physics, 27, pp. 2445-2455; 
Li, L., Zheng, Y., Kallergi, M., Clark, R., Improved method for automatic identification of lung regions on chest radiographs (2001) Academic Radiology, 8, pp. 629-638; 
Monteiro, M., Mendon{\c c}a, A.M., Campilho, A., Lung field detection on chest radiographs (2002) Proceedings of VIIP02, IASTED International Conference on Visualization, Imaging and Image Processing, pp. 367-372. , J. Villanueva (eds.), Spain
Sponsors: IEEE},
	month = {2004///},
	pages = {1287 - 1290},
	address = {Arlington, VA},
	abstract = {The purpose of the research herein presented is the automatic delimitation of lung fields in posterior-anterior digital chest radiographs. In a computer-aided diagnosis system the precise location of the lungs is important as it allows the reduction of the region under analysis, decreasing the computation time and facilitating data compression. Furthermore, it allows the delimitation of the search area, easing the selective tuning of the abnormalities detection algorithms. The results produced by the automatic method were validated by comparison with manual contours traced by experienced radiologists. Two programs with friendly interfaces were developed for this purpose. {\textcopyright} 2004 IEEE.},
	keywords = {Algorithms, Chest radiographs, Computer aided diagnosis, Contour detection, Data compression, Edge detection, Image segmentation, Lung fields, Pulmonary diseases, Radiography, X ray analysis, X ray imaging},
	isbn = {0780383885 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-17144373368\&partnerID=40\&md5=4f13427d02a5013364f4d761ccf5a7b8},
	author = {Mendon{\c c}a, A.M. and Da Silva, J.A. and Campilho, A.}
}
@book {2465,
	title = {Automatic lane and band detection in images of thin layer chromatography},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {3212},
	year = {2004},
	note = {Cited By (since 1996): 7Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Sousa, A.V.; Institute de Engenharia Biom{\'e}dica, Rua Roberto Prias, 4200-465 Porto, Portugal; email: ats@isep.ipp.ptReferences: Sewell, A., (1991) Urinary Oligosaccharides. Techniques in Diagnostic Human Biochemical Genetics, pp. 219-231. , Willey-Liss, Inc;Thomas, G., Disorders of Glycoprotein Degradation. α-Mannosidosis, β-Mannosidosis, Fucosidosis and Sialidosis, , Disorders of Glycoprotein Degradation (Chap. 140); 
Starr, C., Masada, R., Hague, C., Klock, J., Fluorophore-assisted Carbohydrate Electrophoresis in the Separation, Analysis, and Sequencing of Carbohydrates (1996) Journal of Chromatography A, 720, pp. 295-321; 
Klein, A., Lebreton, A., Lemoine, J., P{\'e}rini, J., Roussel, P., Michalski, J., Identification of Urinary Oligosaccharides by Matrix-assisted Laser Desorption Ionization Time-of-flight Mass Spectrometry (1998) Clinical Chemistry, 44 (12), pp. 2422-2428; 
Dempster, A., Laird, N., Rubin, D., Maximum Likelihood from Incomplete Data via the em Algorithm (1977) Journal of the Royal Statistical Society, B, 39 (1), pp. 1-38; 
Schwedt, G., The Essential Guide to Analytical Chemistry, , John Wiley \& Sons; 
Dennis, J., Nonlinear Least-squares, State of the Art in Numerical Analysis, , D. Jacobs Academic Press; 
Byrd, R., Schnabel, R., Shultz, G., Approximate Solution of the Trust Region Problem by Minimization over Two-Dimensional Subspaces (1988) Mathematical Programming, 40, pp. 247-263; 
Branch, M., Coleman, T., Li, Y., A Subspace, Interior, and Conjugate Gradient Method for Large-Scale Bound-Constrained Minimization Problems (1999) SIAM SISC, 21 (1), pp. 1-23; 
Nabney, I., Netlab Algorithms for Pattern Recognition (2003) Advances in Pattern Recognition, , Springer; 
Marques, J., Reconhecimento de Padr{\~o}es, , M{\'e}todos Estatisticos e Neuronais, IST-Press; 
Machado, A., Campos, M., Siqueira, A., Carvalho, O., (1997) An Iterative Algorithm for Segmenting Lanes in Gel Electrophoresis Images; 
},
	month = {2004///},
	pages = {158 - 165},
	abstract = {This work aims at developing an automatic method for the analysis of TLC images for measuring a set of features that can be used for the characterization of the distinctive patterns that result from the separation of oligosaccharides contained in human urine. This paper describes the methods developed for the automatic detection of the lanes contained in TLC images, and for the automatic separation of bands for each detected lane. The extraction of quantitative information related with each band was accomplished with two methods: the EM expectation-maximization and nonlinear least squares trustregion algorithms. The results of these methods, as well as additional quantitative information related with each band, are also presented. {\textcopyright} Springer-Verlag 2004.},
	isbn = {03029743 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33645961076\&partnerID=40\&md5=ef452eea389056fa97b81d62d8f451b9},
	author = {Sousa, A.V. and Aguiar, R. and Mendonca, A.M. and Campilho, A.}
}
@book {2462,
	title = {Bounds for the Average Generalization Error of the Mixture of Experts Neural Network},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {3138},
	year = {2004},
	note = {Cited By (since 1996): 2Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Alexandre, L.A.; Networks and Multimedia Group, IT, Covilh{\~a}, Portugal; email: lfbaa@di.ubi.ptReferences: Gu, H., Takahashi, H., Towards more practical average bounds on supervised learning (1996) IEEE Trans. Neural Networks, 7, pp. 953-968;Vapnik, V., (1999) The Nature of Statistical Learning Theory., , Springer; 
Haykin, S., (1999) Neural Networks: A Comprehensive Foundation, 2nd Edition., , Prentice Hall; 
Alexandre, L., Campilho, A., Kamel, M., Average error bound for the mixture of experts MNN architecture (2002) Proceedings of the 12th Portuguese Conference on Pattern Recognition, , Aveiro, Portugal; 
Jacobs, R., Jordan, M., Nowlan, S., Hinton, G., Adaptive mixtures of local experts (1991) Neural Computation, pp. 79-87; 
Auda, G., Kamel, M., Modular neural network classifiers: A comparative study (1998) J. Intel. Robotic Systems, pp. 117-129; 
Jordan, M., Jacobs, R., Hierarchical mixture of experts and the em algorithm (1994) Neural Computation, pp. 181-214; 
Jacobs, R., Peng, F., Tanner, M., A bayesian approach to model selection in hierarchical mixtures-of-experts architectures (1997) Neural Networks, 10, pp. 231-241; 
De Marques Sa, J., Pattern Recognition: Concepts (2001) Methods and Applications, , Springer; 
Blake, C., Keogh, E., Merz, C., (1998) UCI repository of machine learning databases, , http://www.ics.uci.edu/~mlearn/MLRepository.html; 
Gu, H., Takahashi, H., Estimating learning curves of concept learning (1997) Neural Networks, 10, pp. 1089-1102; 
Bishop, C., (1995) Neural Networks for Pattern Recognition., , Oxford University Press; 
Riedmiller, M., Braun, H., A direct adaptive method for faster backpropagation learning: The RPROP algorithm (1993) IEEE International Conference on Neural Networks, pp. 586-591. , San Francisco
},
	month = {2004///},
	pages = {618 - 625},
	abstract = {In this paper we derive an upper bound for the average-case generalization error of the mixture of experts modular neural network, based on an average-case generalization error bound for an isolated neural network. By doing this we also generalize a previous bound for this architecture that was restricted to special problems. We also present a correction factor for the original average generalization error, that was empirically obtained, that yields more accurate error bounds for the 6 data sets used in the experiments. These experiments illustrate the validity of the derived error bound for the mixture of experts modular neural network and show how it can be used in practice. {\textcopyright} Springer-Verlag 2004.},
	keywords = {Generalization error bounds, Mixture of experts, Modular neural networks},
	isbn = {03029743 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-35048839786\&partnerID=40\&md5=10183183d399e4055c331416faef909e},
	author = {Alexandre, L.A. and Campilho, A. and Kamel, M.}
}
@book {2464,
	title = {Detection of Rib Borders on X-ray Chest Radiographs},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {3212},
	year = {2004},
	note = {Cited By (since 1996): 1Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: INEB - Instituto de Engenharia Biomedica, Laborat{\'o}ries de Sinai, Imagem Biom{\'e}dicaPortugalReferences: Volgelsang, F., Weiler, F., Dahmen, J., Kilbinger, M., Wein, B., G{\"u}nther, R.W., (1998) Detection and Compensation of Rib Structures in Chest Radiographs for Diagnose Assistance, Proceedings of SPIE, 3338, pp. 774-785;Park, M., Jin, J.S., Wilson, L.S., Detection and labeling ribs on expiration chest radiographs (2003) The Phisics of Medical Imaging Conference, SPIE, pp. 1021-1031. , 15-20 Feb; 
Ginneken, B., Romeny, Bart, M.H., Automatic delineation of ribs in frontal chest radiographs (2000) Medical Imaging 2000:Image Processing, Proceedings of SPIE, 3979, pp. 825-836; 
Monteiro, M., Mendon{\c c}a, A.M., Campilho, A., Lung field detection on chest radiographs (2002) Proceedings of VIIP02, pp. 367-372. , IASTED International Conference on Visualization, Imaging and Image Processing, Spain; 
Mendon{\c c}a, A.M., Suva, J., Campilho, A., Automatic delimitation of lung fields on chest radiographs (2004) Proceeding of ISBI04, IEEE International Symposium on Biomedical Imaging, , Arlington, USA; 
Otsu, N., A threshold selection method from gray-level histograms (1979) IEEE Transactions on Systems, Man, and Cybernetics, 9 (1), pp. 62-66
},
	month = {2004///},
	pages = {108 - 115},
	abstract = {The purpose of the research herein presented is the automatic detection of the rib borders in posterior-anterior (PA) digital chest radiographs. In a computer-aided diagnosis system, the precise location of the ribs is important as it allows reducing the false positive in the detection of abnormalities such as nodules, rib lesions and lung lesions. We adopted an edge based approach aiming at detecting the lower border of each rib. For this purpose, the rib geometric model is described as a parabola. For each rib, the upper limit is obtained using the position of the corresponding lower border. {\textcopyright} Springer-Verlag 2004.},
	isbn = {03029743 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-35048903160\&partnerID=40\&md5=8bc25ba1f1b611ae05e55a3597ccdef2},
	author = {Moreira, R. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@article {2501,
	title = {An electronic instrumentation course for non-electronic engineering students},
	journal = {International Journal of Electrical Engineering EducationInt J Electr Eng Educ},
	volume = {41},
	year = {2004},
	note = {Cited By (since 1996): 1Export Date: 21 November 2012Source: ScopusCODEN: IJEEALanguage of Original Document: EnglishCorrespondence Address: Campilho, A.; Inst. de Engenharia Biom{\'e}d., Universidade do Porto, Porto, Portugal; email: campilho@fe.up.ptReferences: (2000) LabVIEW Graphical Programming for Instrumentation, , National Instruments, Austin, Texas, TX, Version 6.1;Consonni, D., Seabra, A.C., A modern approach to teaching basic experimental electricity and electronics (2000) IEEE Trans. Educ., 43, pp. 5-18; 
Goulart, T.J., Consonni, D., Automated system for measurement electrical three-phase power components (2001) IEEE Trans. Educ., 44, pp. 336-341; 
Edward, N.S., The role of laboratory work in engineering education: Student and staff perceptions (2002) Int. J. Elec. Engng. Educ., 39 (1), pp. 11-19; 
Jim{\'e}nez-Leube, F.J., Almendra, A., Gonz{\'a}lez Sanz-Maudes, C., Networked implementation of an electrical measurement laboratory for first course engineering studies (2001) IEEE Trans. Educ., 44, pp. 377-383; 
Campilho, A., (2002) Instrumenta{\c c}{\~a}o Electr{\'o}nica. M{\'e}todos e T{\'e}cnicas de Medi{\c c}{\~a}o, , Edi{\c c}{\~o}es FEUP, Porto
},
	month = {2004///},
	pages = {43 - 55},
	abstract = {This paper reports on the implementation of a required course on electronic instrumentation for informatics engineering students. These students had proved in previous years to require a special approach, as their motivation for this subject was low. The course focuses on the basic concepts of Electronic Instrumentation, including analogue-to-digital and digital-to-analogue conversion and introducing the concept of instrument control. The course was organised in a set of lectures coupled with laboratory experiments, and a small project. To foster collaborative learning all the students work on the same laboratory experiments, simultaneously, organised in groups of two. The students{\textquoteright} feedback on the course was more positive than in previous years, although not substantially so in terms of results. We feel that a change of attitude took place effectively, and that this could be attributed to the laboratory experiments using LabVIEW.},
	keywords = {Analog to digital conversion, Analogue-to-digital conversion, Collaborative learning, Collaborative learning (CL), Curricula, Data processing, Electronic instrumentation, Electronics engineering, Engineering education, LabVIEW, Learning systems, Motivation, Project management, Students},
	isbn = {00207209 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-5444235779\&partnerID=40\&md5=7525d744e353e8757cc0e0ad60988d72},
	author = {Campilho, A. and Cardoso, A.}
}
@book {2461,
	title = {Ribcage Boundary Delineation in Chest X-ray Images},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {3212},
	year = {2004},
	note = {Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Vinhais, C.; INEB - Instituto de Engenharia Biom{\'e}dica, Laborat{\'o}rio de Sinal e Imagem Biom{\'e}dica, Campus da FEUP, Rua Roberto Frias, s/n, 4200-465 Porto, Portugal; email: cav@isep.ipp.ptReferences: Carreira, M., Cabello, D., Computer-Aided Diagnoses: Automatic Detection of Lung Nodules (1998) Med. Phys., 25 (10), pp. 1998-2006;Xu, X., Development of an Improved CAD Scheme for Automated Detection of Lung Nodules in Digital Chest Images (1997) Med. Phys., 25 (9), pp. 1395-1403; 
Lo, S.B., Artificial Convolution Neural Network Techniques and Applications for Lung Nodule Detection (1995) IEEE Trans, on Medical Imaging, 14 (4), pp. 711-718; 
Sanada, S., Doi, K., MacMahon, H., Image feature analysis and computer-aided diagnosis in digital radiography: Automated detection of pneumothorax in chest images (1992) Med. Phys., 19, pp. 1153-1160; 
Nakaromi, N., Doi, K., MacMahon, H., Sasaki, Y., Montner, S.M., Effect on heart-size parameters computed from digital chest radiographs on detection of cardiomegaly: Potencial usefulness for computer-aided diagnosis (1991) Inv. Radiology, 26, pp. 546-550; 
Van Ginneken, B., Romeny, B.H., Viergever, M.A., Computer-aided Diagnosis in Chest Radiography: A survey (2001) IEEE Trans, on Medical Imaging, 20 (12), pp. 1228-1241; 
Xu, X., Doi, K., Image feature analysis for computer-aided diagnosis: Accurate determination of ribcage boundary in chest radiographs (1995) Med. Phys., 22 (5), pp. 617-626; 
Nakaromi, N., Doi, K., Sabeti, V., MacMahon, H., Image feature analysis and computer-aided diagnosis in digital radiography: Automated analysis of sizes of heart and lung in chest images (1990) Med. Phys., 17, pp. 342-435; 
Yue, Z., Goshtasby, A., Ackerman, L.V., Automatic Detection of Rib Borders in Chest Radiographs (1995) IEEE Trans, on Medical Imaging, 14 (3), pp. 525-536; 
Goldberg, D.E., Genetic Algorithms in Search, Optimization \& Machine Learning, pp. 15767-15775. , Reading, MA: Addison Wiley, 1989. - ISBN 0-201-; 
Vinhais, C., Campilho, A., Optimal Detection of Symmetry Axis in Digital Chest X-ray Images (2003) 1st Iberian Conference on Pattern Recognition and Image Analysis - IbPRIA 2003, 2652, pp. 1082-1089. , Lecture Notes in Computer Science, Springer-Verlag, Berlin Heidelberg New York; 
Gofman, Y., Kiryati, N., Detecting Symmetry in Grey Level Images: The Global Optimization Approach (1998) International Journal of Computer Vision (IJCV), 29, pp. 29-45; 
Crist{\'o}bal, G., Navarro, R., Space and frequency variant image enhancement based on Gabor representation (1994) Pattern Recognit. Lett., 15, pp. 273-277; 
Mehrotra, R., Namuduri, K.R., Ranganathan, N., Gabor filter-based edge detection (1992) Pattern Recognit., 25 (12), pp. 1479-1494; 
Petkov, N., Kruizinga, P., Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: Bar and grating cells (1997) Biol. Cybern., 76, pp. 83-96; 
Carpenter, M.B., (1991) Core Text of Neuroanatomy, , 4th ed., Willams \& Wilkins, Baltimore, ISBN 0-683-01457-9
},
	month = {2004///},
	pages = {59 - 67},
	abstract = {We propose a method for segmenting the ribcage boundary of digital postero-anterior chest X-ray images. The segmentation is achieved by first defining image landmarks: the center of the ribcage and, using polar transformation from this point, two initial points belonging to the ribcage. A bank of Gabor filters (in analogy with the simple cells present in the human visual cortex) is used to obtain an orientation edges enhanced image. In this enhanced image, an edge following, starting from the landmarks previously determined, is performed for delineating the left and right sections of the ribcage. The complete segmentation is then accomplished by connecting these sections with the top section of the ribcage, obtained by means of spline interpolation. {\textcopyright} Springer-Verlag 2004.},
	keywords = {Edge following, Gabor Filters, Polar image transform, Probabilistic genetic algorithm, Ribcage boundary segmentation},
	isbn = {03029743 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-35048836666\&partnerID=40\&md5=14b16a871c602045da0d3bf1de34670c},
	author = {Vinhais, C. and Campilho, A.}
}
@conference {2503,
	title = {Detection and validation of lung field contours on chest radiographs},
	booktitle = {Proceedings of the IASTED International Conference on Biomedical EngineeringProc. IASTED Internat. Conf. Biomed. Eng.},
	series = {Proceedings of the IASTED International Conference on Biomedical Engineering},
	year = {2003},
	note = {Conference code: 63063Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Mendon{\c c}a, A.M.; INEB, Inst. de Engenharia Biom{\'e}dica, Campus da FEUP, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal; email: amendon@fe.up.ptReferences: Tsujii, O., Freedman, M., Mun, S., Automated segmentation of anatomic regions in chest radiographs using an adaptive-sized hybrid neural network (1998) Medical Physics, 25, pp. 998-1007;Armato III, S., Giger, M., MacMahon, H., Computerized detection of abnormal asymetry in digital lateral chest radiographs (1994) Medical Physics, 21, pp. 1761-1768; 
Armato III, S., Giger, M., Ashizawa, K., MacMahon, H., Automated lung segmentation in digital lateral chest radiographs (1998) Medical Physics, 25, pp. 1507-1520; 
Carrascal, F., Carreira, J., Souto, M., Automatic calculation of total lung capacity from automatically traced lung boundaries in posterior-anterior and lateral digital chest radiographs (1998) Medical Physics, 25, pp. 1118-1131; 
Ginneken, B., Romeny, B., Automatic segmentation of lung fields in chest radiographs (2000) Medical Physics, 27, pp. 2445-2455; 
Li, L., Zheng, Y., Kallergi, M., Clark, R., Improved method for automatic identification of lung regions on chest radiographs (2001) Academic Radiology, 8, pp. 629-638; 
Monteiro, M., Mendon{\c c}a, A.M., Campilho, A., Lung field detection on chest radiographs (2002) Proceedings of VIIP02, IASTED International Conference on Visualization, Imaging and Image Processing, pp. 367-372. , J. Villanueva (ed): Spain
Sponsors: IASTED},
	month = {2003///},
	pages = {183 - 188},
	address = {Salzburg},
	abstract = {The purpose of the research herein presented is the automatic detection of lung boundaries in posterior-anterior digital chest radiographs. The precise location of the two lungs is important in a computer-aided diagnosis system as it allows the reduction of the region under analysis, decreasing the computation time and facilitating data compression. Furthermore, it allows the delimitation of the search area, easing the selective tuning of the abnormalities detection algorithms. The results produced by the automatic method were validated by comparison with manual contours traced by an experienced radiologist. For this particular purpose, two programs with friendly interfaces were developed. The achieved comparison results demonstrate the good performance of the automatic method.},
	keywords = {Algorithms, automation, Biological organs, Chest radiographs, Computational time, Computer aided diagnosis, Computer-aided diagnosis, Data compression, Image analysis, Medical image processing, Medical imaging, Radiography, Regions of interest (ROI)},
	isbn = {0889863539 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-2642575173\&partnerID=40\&md5=83abd9849c03da498665141b65efa36c},
	author = {Mendon{\c c}a, A.M. and Alves Da Silva, J. and Campilho, A.},
	editor = {Hamza M.H.}
}
@book {2502,
	title = {Optimal detection of symmetry axis in digital chest X-ray images},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {2652},
	year = {2003},
	note = {Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Vinhais, C.; INEB - Institute de Engenharia Biom{\'e}dica, Laborat{\'o}rio de Sinai e Imagem Biom{\'e}dica, Campus da FEUP, Rua Roberto Frias, s/n, 4200-465 Porto, Portugal; email: cav@isep.ipp.ptReferences: Marola, G., On the Detection of the Axes of Symmetry of Symmetric and Almost Symmetric Planar Images (1989) IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, pp. 104-108;Marola, G., Using Symmetry for Detecting and Locating Objects in a Picture (1989) Computer Vision, Graphics and Image Processing, 46, pp. 179-195; 
Zabrodsky, H., Peleg, S., Avnir, D., A Measure of Symmetry Based on Shape Similarity (1992) Proceedings of Computer Vision and Pattern Recognition, pp. 703-706; 
Zabrodsky, H., Peleg, S., Avnir, D., Hierarchical Symmetry (1992) Proceedings of International Conference on Pattern Recognition, 3, pp. 9-11; 
O{\textquoteright}Mara, D., Owens, R., Measuring Symmetry in Digital Images (1996) Proceedings of TENCOM {\textquoteright}96-IEEE Region Ten Conference: Digital Signal Processing Applications, 1, pp. 151-156; 
Sun, C., Symmetry Detection using Gradient Information (1995) Pattern Recognition Letters, 16 (9), pp. 987-996; 
Sun, C., Si, D., Fast Reflectional Symmetry Detection using Orientation Histograms (1999) Real-Time Imaging, 5, pp. 63-74; 
Shen, D., Ip, H.H.S., Cheung, K.K.T., Teoh, E.K., Symmetry Detection by Generalized Complex (GC) Moments: A Close-form Solution (1999) IEEE Transactions on Pattern Analysis and Machine Intelligence, 21 (5), pp. 466-476; 
Shen, D., Ip, H.H.S., Teoh, E.K., An Energy of Asymmetry for Accurate Detection of Global Reflection Axes (2001) Image and Vision Computing, 19, pp. 283-297; 
Yuen, K.S.Y., Chan, W.W., Two Methods for Detecting Symmetries (1994) Pattern Recognition Letters, 15, pp. 279-286; 
Zielke, T., Brauckmann, M., Von Seelen, W., Intensity and Edge-based Symmetry Detection with an Application to Car-following (1990) Computer Vision, Graphics and Image Processing: Image Understanding, 58, pp. 177-190; 
Gofman, Y., Kiryati, N., Detecting Symmetry in Grey Level Images: The Global Optimization Approach (1998) International Journal of Computer Vision (IJCV), 29, pp. 29-45; 
Masuda, T., Yamamoto, K., Yamada, H., Detection of Partial Symmetry using Correlation with Rotated-reflected Images (1993) Pattern Recognition, 26 (8), pp. 1245-1253
},
	month = {2003///},
	pages = {1082 - 1089},
	abstract = {We present a method for detecting the axis of bilateral symmetry in a digital chest X-ray image and subsequently measuring the degree of symmetry of the image. The detection is achieved by analysing rotated-reflected digital chest X-ray images and it is posed as a global optimization problem solved with a probabilistic genetic algorithm (PGA). The global search is initially based on natural peak orientation information related to the orientation of the symmetry axis. Only a few generations of the PGA are needed to achieve convergence to all the images in the database. This method is applied directly on the intensity input image and does not require any prior segmentation. {\textcopyright} Springer-Verlag Berlin Heidelberg 2003.},
	keywords = {image processing, Probabilistic genetic algorithm, Reflectional Symmetry},
	isbn = {03029743 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-35248899557\&partnerID=40\&md5=f8181de2e6823d44786341f3d6642aed},
	author = {Vinhais, C. and Campilho, A.}
}
@book {2504,
	title = {A probabilistic model for the cooperative modular neural network},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {2652},
	year = {2003},
	note = {Cited By (since 1996): 2Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Alexandre, L.A.; Departamento de Inform{\'a}tica, Universidade da Beira Interior, Covilh{\~a}, Portugal; email: lfbaa@di.ubi.ptReferences: Auda, G., Kamel, M., Modular neural network classifiers: A comparative study (1998) J. Intel. Robotic Systems, pp. 117-129;De Bollivier, M., Gallinari, P., Thiria, S., Cooperation of neural nets and task decomposition (1991) Int. Joint Conf. on Neural Networks, 2, pp. 573-576. , Seattle, USA; 
Jacobs, R., Jordan, M., Nowlan, S., Hinton, G., Adaptive mixtures of local experts (1991) Neural Computation, pp. 79-87; 
Jacobs, R., Peng, F., Tanner, M., A bayesian approach to model selection in hierarchical mixtures-of-experts architectures (1997) Neural Networks, 10, pp. 231-241; 
Wanas, N., Kamel, M., Auda, G., Karray, F., Feature-based decision aggregation in modular neural network classifiers (1999) Pattern Recognition Letters, 20, pp. 1353-1359; 
Alexandre, L., Campilho, A., Kamel, M., Combining independent and unbiased classifiers using weighted average (2000) Proceedings of the 15th International Conference on Pattern Recognition, 2, pp. 495-498. , Barcelona, Spain, IEEE Press; 
Alexandre, L., Campilho, A., Kamel, M., On combining classifiers using sum and product rules (2001) Pattern Recognition Letters, 22, pp. 1283-1289; 
Kittler, J., Hatef, M., Duin, R., Matas, J., On combining classifiers (1998) IEEE Trans. PAMI, 20, pp. 226-239; 
Auda, G., Kamel, M., CMNN: Cooperative modular neural networks for pattern recognition (1997) Pattern Recognition Letters, 18, pp. 1391-1398; 
Auda, G., Kamel, M., Raafat, H., Voting schemes for cooperative neural network classifiers (1995) IEEE Int. Conference on Neural Networks, 3, pp. 1240-1243. , Australia
},
	month = {2003///},
	pages = {11 - 18},
	abstract = {This paper presents a model for the probability of correct classification for the Cooperative Modular Neural Network (CMNN). The model enables the estimation of the performance of the CMNN using parameters obtained from the data set. The performance estimates for the experiments presented are quite accurate (less than 1\% relative difference). We compare the CMNN with a multi-layer perceptron with equal number of weights and conclude that the CMNN is preferred for complex problems. We also investigate the error introduced by one of the CMNN voting strategies. {\textcopyright} Springer-Verlag Berlin Heidelberg 2003.},
	isbn = {03029743 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-34547529574\&partnerID=40\&md5=fe30a6406b8bc4deb9b0cc36ab961d59},
	author = {Alexandre, L.A. and Campilho, A. and Kamel, M.}
}
@conference {2555,
	title = {A computational model of visual cortex receptive fields using spatio-temporal filters},
	booktitle = {Proceedings of the IEEE/EMBS Region 8 International Conference on Information Technology Applications in Biomedicine, ITABProc IEEE EMBS Reg 8 Int Conf Inf Technol Appl Biomed ITAB},
	series = {ITAB-ITIS 2000},
	year = {2000},
	note = {Conference code: 58015Cited By (since 1996): 1Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Milanova, M.G.; INEB, Inst. of Biomedical Engineering, University of Porto, Pra{\c c}a Coronel Pacheco, 1 4050 Porto, Portugal; email: milanova@ineb.fe.up.ptReferences: Hubel, D.H., Wiesel, T.N., Receptive fields, binocular interaction, and functional architecture in the cat{\textquoteright}s visual cortex (1962) Journal of Physiology (London), 160, pp. 106-154;DeAngelis, G.C., Ohzawa, I., Freeman, R.D., Receptive-field dynamic in the central visual pathways (1995) Trends in Neuroscience, 18, pp. 451-458; 
Atick, J., Redlich, N., What does the retina know about natural scenes? (1992) Neural Computation, 4, pp. 196-210; 
Barlow, H.B., What is the computational goal of the neocortex? (1994) Large-Scale Neuronal Theories of the Brain, pp. 1-22. , C. Koch and J. L. Davis, eds, Cambridge, MA, MIT Press; 
Olshausen, B.A., Field, D.J., Emergence of simple-cell receptive field properties by learning a sparse code for natural images (1996) Nature, 381, pp. 607-609; 
Rao, R.P.N., Ballard, D.H., Dynamic model of visual recognition predicts neural response properties in the visual cortex (1997) Neural Computation, 9, pp. 721-763; 
Olshausen, B.A., (2000) Sparse coding of time-varying natural images, , http://psyweb.ucdavis.edu/olshausen; 
Oja, E.E., Neural networks, principal components and subspaces (1989) International Journal of Neural Systems, 1, pp. 61-68; 
Field, D.J., What is the goal of sensory coding? (1994) Neural Computation, 6, pp. 559-601; 
Field, D.J., Relations between the statistics of natural images and the response properties of cortical cells (1987) J. Opt. Soc. Am.A, 4, pp. 2379-2394; 
Bell, A.J., Sejnowski, T.J., The independent components of natural scenes are edge filters (1997) Vision Research, 37, pp. 3327-3338; 
Smith, A.T., Snowdon, R.S., (1994) Visual Detection of Motion, , Academic Press Inc., San Diego; 
Zemel, R.S., Sejnowski, T.J., A model for encoding multiple object motions and self-motion in area MST of primate visual cortex (1998) Journal of Neuroscience, 18, pp. 531-547; 
Van Hateren, J.H., Ruderman, D.L., Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex (1998) Proc. Royal Soc. London B, 265, pp. 2315-2320; 
Jones, J.P., Palmer, L.A., The two-dimensional spatial structure of simple receptive fields in cat striate cortex (1987) Journal of Neurophysiology, 58, pp. 1187-1211; 
Jones, J.P., Palmer, L.A., An Evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex (1987) Journal of Neurophysiology, 58, pp. 1233-1258; 
Jones, J.P., Stepnoski, A., Palmer, L.A., The two-dimensional spectral structure of simple receptive fields in cat striate cortex (1987) Journal of Neurophysiology, 58, pp. 1212-1232; 
Lewicki, M.S., Olshausen, B.A., Probabilistic framework for the adaptation and comparison of image codes (1999) Journal of the Optical Society of America, 16, pp. 1587-1601; 
Chua, L.O., Yang, L.L., Cellular Neural Networks: Theory and Applications (1988) IEEE Trans. on Circuits and Systems, 35, pp. 1257-1290
Sponsors: IEEE},
	month = {2000///},
	pages = {129 - 134},
	address = {Arlington, VA},
	abstract = {It is known that neurons in the primary visual cortex are characterized by receptive fields that are selective for specific spatial orientations. It is believed that the sense of object motion and velocity are also related to these fields, as objects in natural scenes are represented by a sparse set of statistically independent components, such as edges. Currently, computational models of receptive fields consider only spatial components, and thus cannot account for time-varying sensory stimuli. In this paper, a model based on independent components analysis and cellular neural networks is proposed. This approach extends existing models to incorporate temporal aspects of sequences of images of natural scenes.},
	keywords = {Cellular neural networks, Computational methods, ICA, Independent component analysis, Mathematical models, Neurology, Receptive fields, Receptive fields (RF), Spatial filters, Spatio-temporal filters, Visual modeling},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0034451070\&partnerID=40\&md5=f1377b4b2f2403be91a21d0feb639fb7},
	author = {Milanova, M.G. and Elmaghraby, A.S. and Wachowiak, M.P. and Campilho, A.}
}
