



@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.}
}
@article {2879,
	title = {E-cadherin and adherens-junctions stability in gastric carcinoma: Functional implications of glycosyltransferases involving N-glycan branching biosynthesis, N-acetylglucosaminyltransferases III and v},
	journal = {Biochimica et Biophysica Acta - General Subjects},
	volume = {1830},
	year = {2013},
	note = {Cited By (since 1996):1Export Date: 12 December 2013Source: Scopus},
	month = {2013///},
	pages = {2690 - 2700},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84873589547\&partnerID=40\&md5=ea238ee2045a2bbfb910b24526aac863},
	author = {Pinho, S.S. and Figueiredo, J. and Cabral, J. and Carvalho, S. and Dourado, J. and Magalh{\~a}es, A. and G{\"a}rtner, F. and Mendon{\c c}a, A.M. and Isaji, T. and Gu, J. and Carneiro, F. and Seruca, R. and Taniguchi, N. and Reis, C.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; 
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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; 
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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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Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Fletcher, E., Kennedy, L., Optic Nerve head Segmentation (2004) IEEE Trans. Medical Imaging, 23 (2), pp. 256-264; 
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; 
Hoover, A., Glodbaum, M., Locating the Optic Nerve in a Retinal Image Using the Fuzzy Convergence of the Blood Vessels (2003) IEEE Trans. Medical Imaging, 22 (8), pp. 951-958; 
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; 
Youssif, A., Ghalwash, A.Z., Ghoneim, A., Optic Disc Detection from Normalized Digital Fundus Images by Means of a Vessels{\textquoteright} Direction Matched Filter (2008) IEEE Trans. Medical Imaging, 27 (1), pp. 11-18; 
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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; 
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},
	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; 
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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 {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.}
}
@conference {2226,
	title = {Automatic segmentation of chromatographic images for region of interest delineation},
	booktitle = {Progress in Biomedical Optics and Imaging - Proceedings of SPIEProgr. Biomed. Opt. Imaging Proc. SPIE},
	series = {Medical Imaging 2011: Image Processing},
	volume = {7962},
	year = {2011},
	note = {Conference code: 84913Cited By (since 1996): 1Export Date: 21 November 2012Source: ScopusArt. No.: 79623Bdoi: 10.1117/12.877671Language of Original Document: EnglishCorrespondence Address: Mendon{\c c}a, A. M.; Instituto de Engenharia Biom{\'e}dica, Universidade Do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal; email: amendon@fe.up.ptReferences: Zarate, Y., Hopkin, R., Fabry{\textquoteright}s disease (2008) The Lancet, 372 (9647), pp. 1427-1435;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., Mendonca, 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. 158-216. , Springer; 
Dempster, A., Laird, N., Rubin, D., Maximum Likelihood from incomplete data via the EM algorithm (1977) J. of the Royal Statistical Society. Series B, 39 (1), pp. 1-38; 
Heijden, F., Duin, R.P.W., Ridder, D., Tax, D.M.J., (2004) Classification, Parameter Estimation and State Estimation - An Engineering Approach Using Matlab, , John Wiley \& Sons
Sponsors: The Society of Photo-Optical Instrumentation Engineers (SPIE); Dynasil Corporation/RMD Research; American Association of Physicists in Medicine (AAPM); DQE Instruments, Inc.; Ocean Thin Films, Inc.},
	month = {2011///},
	address = {Lake Buena Vista, FL},
	abstract = {This paper describes a segmentation method for automating the region of interest (ROI) delineation in chromatographic images, thus allowing the definition of the image area that contains the fundamental information for further processing while excluding the frame of the chromatographic plate that does not contain relevant data for disease identification. This is the first component of a screening tool for Fabry disease, which will be based on the automatic analysis of the chromatographic patterns extracted from the image ROI. Image segmentation is performed in two phases, where each individual pixel is finally considered as frame or ROI. In the first phase, an unsupervised learning method is used for classifying image pixels into three classes: frame, ROI or unknown. In the second phase, distance features are used for deciding which class the unknown pixels belong to. The segmentation result is post-processed using a sequence of morphological operators in order to obtain the final ROI rectangular area. The proposed methodology was successfully evaluated in a dataset of 41 chromatographic images. {\textcopyright} 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).},
	keywords = {Automatic analysis, Automatic segmentations, Chromatographic images, Chromatographic plates, Chromatography, Data sets, Distance feature, Fabry disease, Image pixels, Image segmentation, Imaging systems, Medical imaging, Morphological operator, Pixels, Rectangular area, Region of interest, Region of interest delineation, Screening tool, Second phase, Segmentation methods, Segmentation results, Unsupervised learning, Unsupervised learning method},
	isbn = {16057422 (ISSN); 9780819485045 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79958001297\&partnerID=40\&md5=e679e7314308e03c9042ba3909310a7c},
	author = {Mendon{\c c}a, A.M. and Sousa, A.V. and S{\'a}-Miranda, M.C. and Campilho, A.C.}
}
@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.}
}
@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; 
Klemencic, A., Kovacic, S., Pernus, F., Automated segmentation of muscle fiber images using active contour models (1998) Cytometry, 32, pp. 317-326; 
Kobatake, H., Hashimoto, S., Convergence index filter for vector fields (1999) IEEE Trans. Image Process., 8 (8), pp. 1029-1038. , Aug; 
Lehmussola, A., Ruusuvuori, P., Selinummi, J., Huttunen, H., Yli-Harja, O., Computational framework for simulating fluorescence microscope images with cell populations (2007) IEEE Trans. Med. Imag., 26 (7), pp. 1010-1016. , Jul; 
Luc, V., Soille, P., Watersheds in digital spaces: An efficient algorithm based on immersion simulations (1991) IEEE Trans. Pattern Anal. Mach. Intell., 13 (6), pp. 583-598. , Jun; 
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; 
Moore, P., Molloy, D., A survey of computer-based deformable models (2007) Int. Mach. Vis. Image Process. Conf. (IMVIP), pp. 55-66; 
Otsu, N., A threshold selection method from gray-level histograms (1979) IEEE Tran. Syst., Man, Cybern., 9 (1), pp. 62-66. , Jan; 
Pereira, C.S., Fernandes, H., Mendon{\c c}a, A.M., Campilho, A., Detection of lung nodule candidates in chest radiographs (2007) LNCS, 4478, pp. 170-177; 
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; 
Ruusuvuori, P., Lehmussola, A., Selinummi, J., Rajala, T., Huttunen, H., Yli-Harja, O., Benchmark set of synthetic images for validating cell image analysis algorithms (2008) Proc. 16th Eur. Signal Process. Conf., , Lausanne, Switzerland; 
Solorzano, C., Malladi, R., Lelievre, S., Lockett, S., Segmentation of nuclei and cells using membrane related protein markers (2001) J. Microscopy, 201, pp. 404-415; 
Tek, F.B., Dempster, A.G., Kale, I., (2006) Mathematical Morphology:40 Years on, 30, pp. 441-454. , New York: Springer; 
Wei, J., Hagihara, Y., Kobatake, H., Detection of rounded opacities on chest radiographs using convergence index filter (1999) Proc. Int. Conf. Image Anal. Process., pp. 757-761; 
Xiong, G., Zhou, X., Ji, L., Bradley, P., Perrimon, N., Wong, S., Segmentation of drosophila RNAI fluorescence images using level sets (2006) Proc. IEEE Int. Conf. Image Process., pp. 73-76; 
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; 
Yan, P., Zhou, X., Shah, M., Wong, S.T.C., Automatic segmentation of high-throughput RNAi fluorescent cellular images (2008) IEEE Trans. Inf. Technol. Biomed., 12 (1), pp. 109-117. , Jan; 
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. Circuits Syst., 53 (11), pp. 2405-2414. , Nov
},
	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 {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.}
}
@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.}
}
@article {2297,
	title = {Fibronectin-mediated endothelialisation of chitosan porous matrices},
	journal = {BiomaterialsBiomaterials},
	volume = {30},
	year = {2009},
	note = {Cited By (since 1996): 8Export Date: 21 November 2012Source: ScopusCODEN: BIMADdoi: 10.1016/j.biomaterials.2009.06.056PubMed ID: 19615736Language of Original Document: EnglishCorrespondence Address: Amaral, I.F.; INEB - Instituto de Engenharia Biom{\'e}dica, Divis{\~a}o de Biomateriais, Universidade do Porto, Rua do Campo Alegre, 823, 4150-180 Porto, Portugal; email: iamaral@ineb.up.ptChemicals/CAS: chitosan, 9012-76-4; fibronectin, 86088-83-7; Biocompatible Materials; Chitosan, 9012-76-4; FibronectinsReferences: Moon, J.J., West, J.L., Vascularization of engineered tissues: approaches to promote angiogenesis in biomaterials (2008) Current Topics in Medicinal Chemistry, 8 (4), pp. 300-310;Blitterswijk, C.A., Moroni, L., Rouwkema, J., Siddappa, R., Sohier, J., Tissue engineering - an introduction (2008) Tissue engineering, , Blitterswijk C.A. (Ed), Elsevier; 
Ehrbar, M., Zeisberger, S.M., Raeber, G.P., Hubbell, J.A., Schnell, C., Zisch, A.H., The role of actively released fibrin-conjugated VEGF for VEGF receptor 2 gene activation and the enhancement of angiogenesis (2008) Biomaterials, 29 (11), pp. 1720-1729; 
Elbjeirami, W.M., West, J.L., Angiogenesis-like activity of endothelial cells co-cultured with VEGF-producing smooth muscle cells (2006) Tissue Engineering, 12 (2), pp. 381-390; 
Unger, R.E., Peters, K., Wolf, M., Motta, A., Migliaresi, C., Kirkpatrick, C.J., Endothelialization of a non-woven silk fibroin net for use in tissue engineering: growth and gene regulation of hunian endothelial cells (2004) Biomaterials, 25 (21), pp. 5137-5146; 
Fuchs, S., Motta, A., Migliaresi, C., Kirkpatrick, C.J., Outgrowth endothelial cells isolated and expanded from human peripheral blood progenitor cells as a potential source of autologous cells for endothelialization of silk fibroin biomaterials (2006) Biomaterials, 27 (31), pp. 5399-5408; 
Kirkpatrick, C.J., Unger, R.E., Krump-Konvalinkova, V., Peters, K., Schmidt, H., Kamp, G., Experimental approaches to study vascularization in tissue engineering and biomaterial applications (2003) Journal of Materials Science, 14 (8), pp. 677-681; 
Calonder, C., Matthew, H.W.T., Van Tassel, P.R., Adsorbed layers of oriented fibronectin: a strategy to control cell-surface interactions (2005) Journal of Biomedical Materials Research Part A, 75 A (2), pp. 316-323; 
Kouvroukoglou, S., Dee, K.C., Bizios, R., McIntire, L.V., Zygourakis, K., Endothelial cell migration on surfaces modified with immobilized adhesive peptides (2000) Biomaterials, 21 (17), pp. 1725-1733; 
Burdick, J.A., Khademhosseini, A., Langer, R., Fabrication of gradient hydrogels using a microfluidics/photopolymerization process (2004) Langmuir, 20 (13), pp. 5153-5156; 
Garcia, A.J., Get a grip: integrins in cell-biomaterial interactions (2005) Biomaterials, 26, pp. 7525-7529; 
Tomihata, K., Ikada, Y., In vitro and in vivo degradation of films of chitin and its deacetylated derivatives (1997) Biomaterials, 18 (7), pp. 567-575; 
Amaral, I.F., Lamghari, M., Sousa, S.R., Sampaio, P., Barbosa, M.A., Rat bone marrow stromal cells osteogenic differentiation and fibronectin adsorption on chitosan membranes: the effect of the degree of acetylation (2005) Journal of Biomedical Materials Research Part A, 75 A (2), pp. 387-397; 
Nettles, D.L., Elder, S.H., Gilbert, J.A., Potential use of chitosan as a cell scaffold material for cartilage tissue engineering (2002) Tissue Engineering, 8 (6), pp. 1009-1016; 
Oliveira, S.M., Amaral, I.F., Barbosa, M.A., Teixeira, C.C., Engineering endochondral bone: in vitro studies (2009) Tissue Engineering, 15 (3), pp. 625-634; 
Freier, T., Koh, H.S., Kazazian, K., Shoichet, M.S., Controlling cell adhesion and degradation of chitosan films by N-acetylation (2005) Biomaterials, 26 (29), pp. 5872-5878; 
Wang, X.H., Cui, F.Z., Feng, Q.L., Li, J.C., Zhang, Y.H., Preparation and characterization of collagen/chitosan matrices as potential biomaterials (2003) Journal of Bioactive and Compatible Polymers, 18 (6), pp. 453-467; 
Chupa, J.M., Foster, A.M., Sumner, S.R., Madihally, S.V., Matthew, H.W.T., Vascular cell responses to polysaccharide materials: in vitro and in vivo evaluations (2000) Biomaterials, 21 (22), pp. 2315-2322; 
Chung, T.W., Lu, Y.F., Wang, S.S., Lin, Y.S., Chu, S.H., Growth of human endothelial cells on photochemically grafted Gly-Arg-Gly-Asp (GRGD) chitosans (2002) Biomaterials, 23 (24), pp. 4803-4809; 
Cuy, J.L., Beckstead, B.L., Brown, C.D., Hoffman, A.S., Giachelli, C.M., Adhesive protein interactions with chitosan: consequences for valve endothelial cell growth on tissue-engineering materials (2003) Journal of Biomedical Materials Research Part A, 67 A (2), pp. 538-547; 
Huang, Y., Onyeri, S., Siewe, M., Moshfeghian, A., Madihally, S.V., In vitro characterization of chitosan-gelatin scaffolds for tissue engineering (2005) Biomaterials, 26 (36), pp. 7616-7627; 
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Kirsebom, H., Aguilar, M.R., Roman, J.S., Fernandez, M., Prieto, M.A., Bondar, B., Macroporous scaffolds based on chitosan and bioactive molecules (2007) Journal of Bioactive and Compatible Polymers, 22 (6), pp. 621-636; 
Cao, W.L., Jing, D.H., Li, J.M., Gong, Y.D., Zhao, N.M., Zhang, X.F., Effects of the degree of deacetylation on the physicochemical properties and Schwann cell affinity of chitosan films (2005) Journal of Biomaterials Applications, 20 (2), pp. 157-177; 
Amaral, I.F., Sampaio, P., Barbosa, M.A., Three-dimensional culture of human osteoblastic cells in chitosan sponges: the effect of the degree of acetylation (2006) Journal of Biomedical Materials Research Part A, 76 A (2), pp. 335-346; 
Brugnerotto, J., Lizardi, J., Goycoolea, F.M., Arguelles-Monal, W., Desbrieres, J., Rinaudo, M., An infrared investigation in relation with chitin and chitosan characterization (2001) Polymer, 42 (8), pp. 3569-3580; 
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Amaral, I.F., Cordeiro, A.L., Sampaio, P., Barbosa, M.A., Attachment, spreading and short-term proliferation of human osteoblastic cells cultured on chitosan films with different degrees of acetylation (2007) Journal of Biomaterials Science Polymer Edition, 18 (4), pp. 469-485; 
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Unger, R.E., Krump-Konvalinkova, V., Peters, K., Kirkpatrick, C.J., In vitro expression of the endothelial phenotype: comparative study of primary isolated cells and cell lines, including the novel cell line HPMEC-ST1.6R (2002) Microvascular Research, 64 (3), pp. 384-397; 
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Barbosa, J.N., Amaral, I.F., {\'A}guas, A.P., Barbosa, M.A., Evaluation of the effect of the degree of acetylation on the inflammatory response to 3D porous chitosan scaffolds Journal of Biomedical Materials Research Part A, , in press, doi:10.1002/jbm.a.32499; 
Marshall, A.J., Barker, T., Sage, E.H., Hauch, K.D., Ratner, B.D., (2004) Pore size controls angiogenesis in subcutaneously implanted porous matrices. 7th World Biomaterials Congress; 2004, , Australian Society for Biomaterials Inc., Sydney, Australia p. 710; 
Unger, R.E., Sartoris, A., Peters, K., Motta, A., Migliaresi, C., Kunkel, M., Tissue-like self-assembly in cocultures of endothelial cells and osteoblasts and the formation of microcapillary-like structures on three-dimensional porous biomaterials (2007) Biomaterials, 28 (27), pp. 3965-3976; 
Tidwell, C.D., Ertel, S.I., Ratner, B.D., Tarasevich, B.J., Atre, S., Allara, D.L., Endothelial cell growth and protein adsorption on terminally functionalized, self-assembled monolayers of alkanethiolates on gold (1997) Langmuir, 13 (13), pp. 3404-3413; 
Wendt, D., Marsano, A., Jakob, M., Heberer, M., Martin, I., Oscillating perfusion of cell suspensions through three-dimensional scaffolds enhances cell seeding efficiency and uniformity (2003) Biotechnology and Bioengineering, 84 (2), pp. 205-214; 
Santos, M.I., Fuchs, S., Gomes, M.E., Unger, R.E., Reis, R.L., Kirkpatrick, C.J., Response of micro- and macrovascular endothelial cells to starch-based fiber meshes for bone tissue engineering (2007) Biomaterials, 28 (2), pp. 240-248; 
Vestweber, D., Molecular mechanisms that control endothelial cell contacts (2000) Journal of Pathology, 190 (3), pp. 281-291; 
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Panetti, T.S., Hannah, D.F., Avraamides, C., Gaughan, J.P., Marcinkiewicz, Z., Huttenlocher, A., Extracellular matrix molecules regulate endothelial cell migration stimulated by lysophosphatidic acid (2004) Journal of Thrombosis and Haemostasis, 2 (9), pp. 1645-1656; 
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Renner, L., Jorgensen, B., Markowski, M., Salchert, K., Werner, C., Pompe, T., Control of fibronectin displacement on polymer substrates to influence endothelial cell behaviour (2004) Journal of Materials Science-Materials in Medicine, 15 (4), pp. 387-390; 
Garcia, A.J., Vega, M.D., Boettiger, D., Modulation of cell proliferation and differentiation through substrate-dependent changes in fibronectin conformation (1999) Molecular Biology of the Cell, 10 (3), pp. 785-798; 
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Keselowsky, B.G., Collard, D.M., Garcia, A.J., Surface chemistry modulates focal adhesion composition and signaling through changes in integrin binding (2004) Biomaterials, 25, pp. 5947-5954; 
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},
	month = {2009///},
	pages = {5465 - 5475},
	abstract = {Chitosan (Ch) porous matrices were investigated regarding their ability to be colonized by human microvascular endothelial cells (HPMEC-ST1.6R cell line) and macrovascular endothelial cells namely HUVECs. Specifically we assessed if previous incubation of Ch in a fibronectin (FN) solution was effective in promoting endothelial cell (EC) adhesion to Ch matrices with different degrees of acetylation (DAs). Upon FN physiadsorption, marked differences were found between the two DAs investigated, namely DA 4\% and 15\%. While cell adhesion was impaired on Ch with DA 15\%, ECs were able to not only adhere to Ch with DA 4\%, but also to spread and colonize the scaffolds, with retention of the EC phenotype and angiogenic potential. To explain the observed differences between the two DAs, protein adsorption studies using                        125I-FN and immunofluorescent labelling of FN cell-binding domains were carried out. In agreement with the higher cell numbers found, scaffolds with DA 4\% revealed a higher number of exposed FN cell-binding domains as well as greater ability to adsorb FN and to retain and exchange adsorbed FN in the presence of competitive proteins. These findings suggest that the DA is a key parameter modulating EC adhesion to FN-coated Ch by influencing the adsorbed protein layer. {\textcopyright} 2009 Elsevier Ltd. All rights reserved.
},
	keywords = {Acetylation, Adhesion, Adsorption, angiogenesis, article, Biocompatible Materials, Blood vessel prostheses, cell adhesion, Cell culture, cell invasion, Cell Line, Cell lines, Cell membranes, Cell number, cell proliferation, Cell size, cell spreading, cell survival, Cell-binding domain, Chitin, Chitin/chitosan, chitosan, colony formation, controlled study, Degrees of acetylation, Electric conductivity measurement, Electric currents, Endothelial cell, Endothelial Cells, Endothelialisation, endothelium, endothelium cell, epithelization, Fibronectin, Fibronectins, human, human cell, Human microvascular endothelial cells, Humans, immunofluorescence test, Key parameters, Labels, materials testing, Matrix algebra, Neovascularization, Physiologic, phenotype, porosity, Porous matrixs, priority journal, Protein adsorption, Protein layers, Scaffold, Scaffolds, Self assembly, Tissue engineering, tissue scaffold, umbilical vein},
	isbn = {01429612 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-68549107574\&partnerID=40\&md5=9babb57c1a4f2ae6eb7e7e0643bffe48},
	author = {Amaral, I.F. and Unger, R.E. and Fuchs, S. and Mendon{\c c}a, A.M. and Sousa, S.R. and Barbosa, M.A. and P{\^e}go, A.P. and Kirkpatrick, C.J.}
}
@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},
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Bajla, I., Hollander, I., Burg, K., Improvement of electrophoretic gel image analysis (2001) Meas. Sci. Rev, 1, pp. 5-10; 
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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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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; 
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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; 
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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; 
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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; 
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; 
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; 
Meyer, F., Beucher, S., Morphological segmentation (1990) JVCIR, 1 (1), pp. 21-46; 
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. In: Proc. of the Tenth Australian Joint Conf. on Artificial Intelligence (December 1997)Burges, C., A tutorial on support vector machines for pattern recognition (1998) Data Mining and Knowledge Discovery, 2 (2), pp. 121-167
},
	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 {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.}
}
@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.}
}
@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 {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.}
}
@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.}
}
@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 {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.}
}
@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.}
}
