



@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; 
Osareh, A., Mirmehdi, M., Thomas, B., Markham, R., Classification and Localisation of Diabetic-Related Eye Disease (2002) LNCS, 2353, pp. 502-516. , Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. Springer, Heidelberg; 
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; 
Niemeijer, M., Abr{\`a}moff, M.D., Van Ginneken, B., Fast detection of the optic disc and fovea in color fundus photographs (2009) Medical Image Analysis, 13, pp. 859-870; 
Xu, X., Gravin, M.K., Abr{\`a}moff, M.D., Reinhardt, J.M., Simultaneous Automatic Detection of the Optic Disc and Fovea on Fundus Photographs (2011) Proc. SPIE Medical Imaging 2011: Image Processing, 7962, pp. 7962T. , Dawant, B.M., Haynor, D.R. (eds.) SPIE; 
http://www.isi.uu.nl/Research/Databases/DRIVEhttp://www.ces.clemson.edu/~ahoover/stare, The STARE projectMendon{\c c}a, A., Campilho, A., Segmentation of Retinal Blood Vessels by Combining the Detection of Centerlines and Morphological Reconstruction (2006) IEEE Trans. Medical Imaging, 25 (9), pp. 1200-1213; 
Chaudhuri, S., Chattererjee, S., Katz, N., Glodbaum, M., Detection of blood Vessels in Retinal Images using Two-Dimensional Matched Filters (1989) IEEE Trans. Medical Imaging, 28 (3), pp. 263-269
},
	month = {2012///},
	pages = {424 - 431},
	address = {Aveiro},
	abstract = {This paper proposes an automatic method for estimating the location of the optic disc in color images of the retina. The proposed methodology is founded in a new concept, the entropy of vascular directions, which proved to be a reliable measure for assessing the convergence of vessels around an image point. To improve the robustness of the method, the search for the maximum value of entropy is restricted to image areas with high intensity. This new method was evaluated in two publicly available databases, containing both normal and pathological images, and was able to obtain a valid location for the optic disc in 115 out of the 121 images of the two datasets. {\textcopyright} 2012 Springer-Verlag.},
	keywords = {Automatic localization, Automatic method, Color images, Data sets, entropy, High intensity, Image analysis, Image points, Maximum values, Ophthalmology, optic disc, Pathological images, Retinal image, Retinal image analysis, vessel segmentation},
	isbn = {03029743 (ISSN); 9783642312977 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84864128693\&partnerID=40\&md5=faf6f5351e81f8c595247728be684d95},
	author = {Mendon{\c c}a, A.M. and Cardoso, F. and Sousa, A.V. and Campilho, A.}
}
@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.}
}
@conference {2219,
	title = {Low frame rate cell tracking: A Delaunay graph matching approach},
	booktitle = {Proceedings - International Symposium on Biomedical ImagingIEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn.},
	series = {2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI{\textquoteright}11},
	year = {2011},
	note = {Conference code: 87101Export Date: 21 November 2012Source: ScopusArt. No.: 5872573doi: 10.1109/ISBI.2011.5872573Language of Original Document: EnglishCorrespondence Address: Chen, Y.; INEB - Instituto de Engenharia Biom{\'e}dica, Rua do Campo Alegre, 823, 4150-180 Porto, PortugalReferences: Godinez, W.J., Lampe, M., Worz, S., Muller, B., Eils, R., Rohr, K., Tracking of virus particles in time-lapse fluorescence microscopy image sequences (2007) Proceedings of the IEEE International Symposium on Biomedical Imaging, pp. 256-259;Finch, A.W., Wilson, R.C., Hancock, E.R., Symbolic graph matching with the em algorithm (1998) Pattern Recognit, 31 (11), pp. 1777-1790. , Nov; 
Finch, M.A., Wilson, C.R., Hancock, R.E., Matching delaunay graphs (1997) Pattern Recognit, 30 (1), pp. 123-140. , Nov; 
Quelhas, P., Marcuzzo, M., Mendon{\c c}a, A.M., Campilho, A., Cell nuclei and cytoplasm joint segmentation using the sliding band filter (2010) IEEE Transactions on Medical Imaging, 29, pp. 1463-1473; 
Fortune, S., A sweepline algorithm for voronoi diagram (1987) Algorithmica, 2, pp. 153-174; 
Khoo, K., Suganthan, P., Evaluation of genetic operators and solution representations for shape recognition by genetic algorithms (2002) Patt. Recognit. Lett., 23 (13), pp. 1589-1597; 
Caetano, T.S., Caelli, T., Schuurmans, D., Barone, D., Graphical models and point pattern matching (2006) IEEE Trans. on Pattern Analysis and Machine Intelligence, 28 (10), pp. 1646-1662. , Oct; 
Raveaux, R., Burie, J.-C., Ogier, J.-M., A graph matching method and a graph matching distance based on subgraph assignments (2010) Pattern Recognition Letters, 31, pp. 394-406; 
Marcuzzo, M., Quelhas, P., Campilho, A., Cell division detection on the arabidopsis thaliana root (2009) IbPRIA 2009, 5524, pp. 168-175. , LNCS
Sponsors: Inst. Electr. Electron. Eng., Eng. Med. Biol. Soc. (IEEE EMBS); Inst. Electr. Electron. Eng., Signal Process. Soc. (IEEE SPS)},
	month = {2011///},
	pages = {1015 - 1018},
	address = {Chicago, IL},
	abstract = {Cell tracking is a fundamental problem for studying live cell dynamics. A novel approach for low frame rate cell tracking in fluorescent microscopy image is herein proposed. The method is based on sliding band filter detection and Delaunay triangulation sub-graph matching. With this approach we can track a large amount of small cells without their motion model and not relying on cell motion continuity between consecutive images. The effectiveness and robustness of the method were validated by visual inspection and on a ground truth dataset. {\textcopyright} 2011 IEEE.},
	keywords = {Band filters, cell motion, Cell tracking, Cells, Consecutive images, Data sets, Delaunay graph, Delaunay graph matching, Delaunay triangulation, Fluorescent microscopy, Frame rate, Fundamental problem, Ground truth, Live cell, low frame rate tracking, Medical imaging, Motion models, small target tracking, Small targets, Target tracking, Visual inspection},
	isbn = {19457928 (ISSN); 9781424441280 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-80055027991\&partnerID=40\&md5=f347799ea22097a5550939fa287cd4ab},
	author = {Chen, Y. and Quelhas, P. and Campilho, A.}
}
@article {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; 
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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 {2331,
	title = {The influence of the risk functional in data classification with MLPs},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {5163 LNCS},
	year = {2008},
	note = {Conference code: 73798Cited By (since 1996): 2Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-540-87536-9_20Language of Original Document: EnglishCorrespondence Address: Silva, L. M.; INEB -Instituto de Engenharia Biom{\'e}dica, Divis{\~a}o de Sinal e Imagem, Campus FEUP, Rua Dr. Roberto Frias, 4200 - 465 Porto, Portugal; email: lmsilva@fe.up.ptReferences: Silva, L.M., Alexandre, L.A., Marques de S{\'a}, J.: Data classification with multilayer perceptrons using a generalized error function. Neural Networks (2008) doi:10.1016/j.neunet.2008.04,004Erdogmus, D., Principe, J.C., Generalized information potential criterion for adaptive system training (2002) IEEE Transactions on Neural Networks, 13 (5), pp. 1035-1044;Erdogmus, D., Principe, J.C., An Error-Entropy Minimization Algorithm for Supervised Training of Nonlinear Adaptive Systems (2002) IEEE Transactions on Signal Processing, 50 (7), pp. 1780-1786; 
Santos, J.M., Alexandre, L.A., Marques de S{\'a}, J., The Error Entropy Minimization Algorithm for Neural Network Classification (2004) Int. Conf. on Recent Advances in Soft Computing, , Nottingham, United Kingdom; 
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Embrechts, M.J., Szymanski, B., Sternickel, M., Introduction to Scientific Data Mining: Direct Kernel Methods and Applications (2004) Computationally Intelligent Hybrid Systems, pp. 317-363. , ch. 10, pp, Wiley Interscience, Chichester; 
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},
	month = {2008///},
	pages = {185 - 194},
	address = {Prague},
	abstract = {We investigate the capability of multilayer perceptrons using specific risk functionals attaining the minimum probability of error (optimal performance) achievable by the class of mappings implemented by the multilayer perceptron (MLP). For that purpose we have carried out a large set of experiments using different risk functionals and datasets. The experiments were rigorously controlled so that any performance difference could only be attributed to the different risk functional being used. Statistical analysis was also conducted in a careful way. From the several conclusions that can be drawn from our experimental results it is worth to emphasize that a risk functional based on a specially tuned exponentially weighted distance attained the best performance in a large variety of datasets. As to the issue of attaining the minimum probability of error we also carried out classification experiments using non-MLP classifiers that implement complex mappings and are known to provide the best results until this date. These experiments have provided evidence that at least in many cases, by using an adequate risk functional, it will be possible to reach the optimal performance. {\textcopyright} Springer-Verlag Berlin Heidelberg 2008.},
	keywords = {Backpropagation, Complex mapping, Data classification, Data sets, Electric loads, Experiments, Functionals, Minimum probability of error, MLP classifiers, Multi layer perceptron, Multi-layer perceptrons, Multilayer neural networks, Multilayers, Optimal performance, Pattern recognition systems, Probability density function, risks, Statistical analysis, Weighted distance},
	isbn = {03029743 (ISSN); 3540875352 (ISBN); 9783540875352 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-58849129862\&partnerID=40\&md5=d9d0d90a540a21b191eebbf1956ed6e9},
	author = {Silva, L.M. and Embrechts, M. and Santos, J.M. and De S{\'a}, J.M.}
}
