



@book {2197,
	title = {Automatic lane detection in chromatography images},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {7325 LNCS},
	year = {2012},
	note = {Conference code: 91199Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-31298-4_22Language of Original Document: EnglishCorrespondence Address: Moreira, B.M.; Instituto de Engenharia Biom{\'e}dica, Universidade Do Porto, Rua Roberto Frias, s/n, 4200-465 Porto, Portugal; email: moreira@fe.up.ptReferences: Fried, B., Sherma, J., (1999) Thin-Layer Chromatography, , Marcel Dekker, New York;Rodrigues, L.G., Ferraz, M.J., Rodrigues, D., Pais-Vieira, M., Lima, D., Brady, R.O., S{\'a}-Miranda, M.C., Neurophysiological behavioral and morphological abnormalities in the Fabry knockout mice (2009) Neurobiology of Disease, 33 (1), pp. 48-56; 
Elstein, D., Altarescu, G., Beck, M., (2010) Fabry Disease, , Springer, Heidelberg; 
Houck, M.M., Siegel, J.A., (2010) Fundamentals of Forensic Science, , Elsevier, Oxford; 
Machado, A.M.C., Campos, M.F.M., Siqueira, A.M., De Carvalho, O.S.F., An iterative algorithm for segmenting lanes in gel electrophoresis images (1997) Proc. X Brazilian Symposium on Computer Graphics and Image Processing, pp. 140-146. , October; 
Elder, J.K., Southern, E.M., Computer-aided analysis of one dimensional restriction fragment gels (1987) Nucleic Acid and Protein Sequence Analysis - A Practical Aproach, pp. 165-172. , Bishop, M.J., Rawlings, C.J. (eds.) IRL Press, Oxford; 
Bajla, I., Holl{\"a}nder, I., Fluch, S., Burg, K., Koll{\'a}r, M., An alternative method for electrophoretic gel image analysis in the GelMaster software (2005) Computer Methods and Programs in Biomedicine, 77, pp. 209-231; 
Lin, C., Ching, Y., Yang, Y., An Automatic Method to Compare the Lanes in Gel Electrophoresis (GE) Images (2007) IEEE Transaction on Information Technology in Biomedicine, 11 (2), pp. 179-189; 
Akbari, A., Fritz, A., Jackobsen, K.S., Automatic lane detection and separation in one dimensional gel images using continuous wavelet transform (2010) The Royal Society of Chemistry, Analytical Methods, 2, pp. 1360-1371; 
Sousa, A.V., Aguiar, R.L., Mendon{\c c}a, A.M., Campilho, A.C., Automatic Lane and Band Detection in Images of Thin Layer Chromatography (2004) LNCS, 3212, pp. 158-165. , Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. Springer, Heidelberg; 
Sotaquira, M., On the use of distance maps in the analysis of 1D DNA gel images (2009) ICDIP 2009: Proceedings of the International Conference on Digital Image Processing, pp. 172-176. , IEEE Computer Society; 
Barrantes, P., Alvarado, P., Lane Detection on Gel Electrophoresis Images using Active Shape Models (2011) Proc. of the Conference on Technologies for Sustainable Development, TSD 2011, pp. 43-46. , February; 
Mendon{\c c}a, A.M., Sousa, A.V., S{\'a}-Miranda, M.C., Campilho, A.C., Automatic segmentation of chromatographic images for region of interest delineation (2011) Proc. SPIE, 7962, pp. 79623B1-79623B7; 
Chau, F., Liang, Y., Gao, J., Shao, X., (2004) Chemometrics - from Basics to Wavelet Transform, , John Wiley \& Sons, New Jersey; 
Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T., (1992) Numerical Recipes in C: The Art of Scientific Computing, , Syndicate of the University of Cambridge; 
Soille, P., Jones, C.D., Smith, A.B., Roberts, E.F., (2004) Morphological Image Analysis - Principles and Applications, , Springer; 
Visa, S., Ralescu, A., Learning Imbalanced and Overlapping Classes using Fuzzy Sets Workshop on Learning from Imbalanced Datasets II, ICML (2003)
},
	month = {2012///},
	pages = {180 - 187},
	address = {Aveiro},
	abstract = {This paper proposes a method for automating the detection of lanes in Thin-Layer Chromatography images. Our approach includes a preprocessing step to detect the image region of interest, followed by background estimation and removal. This image is then projected onto the horizontal direction to integrate the information into a one-dimensional profile. A smoothing filter is applied to this profile and the outcome is the input of the lane detection process, which is performed in three phases. The first one aims at obtaining an initial set of candidate lanes that are further validated or removed in the second phase. The last phase is a refinement step that allows the inclusion of lanes that are not clearly distinguishable in the profile and that were not included in the initial set. The method was evaluated in 66 chromatography images and achieved values of recall, precision and F                        β -measure of 97.0\%, 99.4\% and 98.2\%, respectively. {\textcopyright} 2012 Springer-Verlag.
},
	keywords = {Background estimation, Bio-medical image processing, Biomedical image processing, Chromatography, Chromatography images, Computer aided diagnosis, Image analysis, Image regions, Lane detection, Pre-processing step, Refinement step, Second phase, Smoothing filters, Three phasis},
	isbn = {03029743 (ISSN); 9783642312977 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84864120489\&partnerID=40\&md5=1e51865766ec865c2127c5b672f97b8f},
	author = {Moreira, B.M. and Sousa, A.V. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@book {2198,
	title = {Automatic localization of the optic disc in retinal images based on the entropy of vascular directions},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {7325 LNCS},
	year = {2012},
	note = {Conference code: 91199Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-31298-4_50Language of Original Document: EnglishCorrespondence Address: Mendon{\c c}a, A.M.; Instituto de Engenharia Biom{\'e}dica, Universidade Do Porto, Rua Roberto Frias, s/n, 4200-465 Porto, Portugal; email: amendon@fe.up.ptReferences: Jelinek, H., Cree, M., (2010) Automated Image Detection of Retinal Pathology, , CRC Press, Taylor \& Francis Group, Boca Raton;Couper, D.J., Klein, R., Hubbard, L.D., Wong, T.Y., Sorlie, P., Cooper, L.S., Brothers, R.J., Nieto, F.J., Reliability of Retinal Photography in the Assessment of Retinal Microvascular Characteristics: The Atherosclerosis Risk in Communities Study (2002) American Journal of Ophthalmology, 133 (1), pp. 78-88; 
Sinthanayothin, C., Boyce, J.F., Cook, H.L., Williamson, T.H., Automated localization of the optic disc, fovea, and retinal blood vessels from digital colour fundus images (1999) Br. J. Ophthalmol., 83, pp. 902-910; 
Aquino, A., Geg{\'u}ndez-Arias, M.E., Marin, D., Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection and Feature Extraction Techniques (2010) IEEE Trans. Medical Imaging, 29 (11), pp. 1860-1869; 
Aquino, A., Geg{\'u}ndez-Arias, M.E., Marin, D., Automated Optic Disc Detection in Retinal Images of Patients with Diabetic Retinopathy and Risk of Macular Edema (2012) Int. J. of Biological and Life Sciences, 8 (2), pp. 87-92; 
Lalonde, M., Beaulieu, M., Gagnon, L., Fast and Robust Optic Disc Detection Using Pyramidal Decomposition and Hausdorff-based Template Matching (2001) IEEE Trans. Medical Imaging, 20 (11), pp. 1193-1200; 
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.}
}
@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.}
}
@article {2348,
	title = {Chromatographic pattern classification},
	journal = {IEEE Transactions on Biomedical EngineeringIEEE Trans. Biomed. Eng.},
	volume = {55},
	year = {2008},
	note = {Cited By (since 1996): 5Export Date: 21 November 2012Source: ScopusArt. No.: 8CODEN: IEBEAdoi: 10.1109/TBME.2008.919852PubMed ID: 18714832Language of Original Document: EnglishCorrespondence Address: Sousa, A. V.; Instituto de Engenharia Biom{\'e}dica (INEB), Instituto Superior de Engenharia Do Porto, 4200-465 Porto, Portugal; email: ats@isep.ipp.ptChemicals/CAS: Hydrolases, EC 3.-References: Keir, G., Winchester, B., Clayton, P., Carbohydrate-deficient glycoprotein syndromes: Inborn errors of protein glycosylation (1999) Ann. Clin. Biochem, 36 (1), pp. 20-36;Durand, G., Seta, N., Protein glycosylation and diseases: Blood and urinary oligosaccharides as markers for diagnosis and therapeutic monitoring (2000) Clin. Chem, 46, pp. 795-805; 
Scriver, S., Beaudet, A., Sly, W., Valle, D., (2001) The Metabolic Bases of Inherited Disease, 3. , New-York: McGraw-Hill; 
Pinto, R., Caseiro, C., Lemos, M., Lopes, L., Fontes, A., Miranda, M., Prevalence of lysosomal storage diseases in Portugal (2004) Eur. J. Hum. Genet, 12, pp. 87-92; 
Friedman, R., Williams, M., Moser, H., Kolodny, E., Improved thin-layer chromatographic method in the diagnosis of mannosidosis (1978) Clin. Chem, 24, pp. 1576-1577; 
Kurczynski, T., Kendzierski, K., Sewell, A., Kuczynski, T., Urinary oligosaccharides in pregnant or lactating women: Pitfall in screening (1993) Clin. Chem, 39, pp. 2346-2347; 
Sewell, A., Urinary oligosaccharides (1991) Techniques Diagnostic Human Biochemical Genetics, pp. 219-231. , New York: Wiley; 
Reich, E., Blatter, A., Modern TLC: A key technique for identification and quality control of botanicals and dietary supplements (2005) J. Planar Chromatogr. - Modern TLC, 18, pp. 34-37. , Jan./Feb; 
Duvdevani-Bar, S., Edelman, S., Visual recognition and categorization on the basis of similarities to multiple class prototypes (1999) Int. J. Comput. Vis, 33, pp. 201-228; 
Pekalska, E., Duin, R., Dissimilarity representations allow for building good classifiers (2002) Pattern Recognit. Lett, 23, pp. 943-956; 
Goldstone, R., Son, J., Similarity (2005) Cambridge Handbook of Thinking and Reasoning, pp. 13-36. , K. Holyoak and R. Morrison, Eds. Cambridge, MA: Cambridge Univ. Press; 
Lavine, B., Clustering and classification of analytical data (2000) Encyclopedia of Analytical Chemistry: Instrumentation and Applications, pp. 9689-9710. , R. Meyers, Ed. Chichester, U.K, Wiley; 
Japkowicz, N., Learning from imbalanced data sets: A comparison of various strategies (2000) Proc. Learn. Imbalanced Data, pp. 10-15; 
Weiss, G., Mining with rarity: A unifying framework (2004) Sigkdd Explor, 6, pp. 7-19; 
Kubat, M., Holte, R., Matwin, S., Machine learning for the detection of oil spills in satellite radar images (1998) Mach. Learn, 30, pp. 195-215; 
Tax, D., One-class classification; concept-learning in the absence of counter-examples, (2001), ASCI, pp. 1-190. , Ph.D. dissertation, Delft Univ. Technol, Dissertation Ser, ppSousa, A., Aguiar, R., Mendon{\c c}a, A., Campilho, A., Automatic lane and band detection in images of thin layer chromatography (2004) Lecture Notes in Computer Science, 3212, pp. 158-165. , Image Analysis and Recognition, New York: Springer-Verlag; 
Schwedt, G., (1997) The Essential Guide to Analytical Chemistry, , New York: Wiley; 
Bajla, I., Hollander, I., Fluch, S., Burg, K., Kollar, M., An alternative method for electrophoretic gel image analysis in the GelMaster software (2005) Comput. Methods Programs Biomed, 77, pp. 209-231; 
Eibrand, R., Kennedy, P., Cotter, D., MacEvilly, U., Wu, B., Analysis of Atlantic salmon skin mucus: COPS - A computer-based system for protein pattern analysis of 1D SDS-PAGE gels (2003) Proc. IEEE Symp. Bioinf. BioEng, pp. 45-52; 
Gerasimov, A., Use of the software processing of scanned chromatogram images in quantitative planar chromatography (2004) J. Anal. Chem, 59, pp. 348-353; 
Goulding, P., (2000) Electrophoresis Gel Analysis Software: Important Aspects, p. 14. , Cambridge, U.K, 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., Campos, M., Siqueira, A., Carvalho, O., An iterative algorithm for segmenting lanes in gel electrophoresis images (1997) Proc. Comput. Graph. Image Process. X Braz. Symp, pp. 140-146. , Campos do Jordao, Brazil, Oct; 
Ye, X., Suen, C., Cheriet, M., Wang, E., A recent development in image analysis of electrophoresis gels (1999) Proc. Vis. Interface, pp. 432-438; 
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 a protein-DNA binding constant (1997) Nucleic Acids Res, 25, pp. 850-860; 
Bajla, I., Hollander, I., Burg, K., Improvement of electrophoretic gel image analysis (2001) Meas. Sci. Rev, 1, pp. 5-10; 
Bajla, I., Hollander, I., Burg, K., Fluch, S., A novel approach to quantitative analysis of electrophoretic gel images of DNA fragments (2002) Proc. IEEE Int. Symp. Biomed. Imag, pp. 899-902; 
Lonni, A., Scarminio, I., Silva, L., Ferreira, D., Differentiation of species of Baccharis Genus by HPLC and chemometrics methods (2003) Anal. Sci, 19, pp. 1013-1017; 
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; 
Landgrebe, D., Information extraction principles and methods for multispectral and hyperspectral image data (2000) Information Processing for Remote Sensing, , Hackensack, NJ: World Scientific, ch. 1; 
Landgrebe, D., Hyperspectral image data analysis as a high dimensional signal processing problem (2002) IEEE Signal Process. Mag, 19 (1), pp. 17-28. , Jan; 
Jimenez, L., Landgrebe, D., Hyperspectral data analysis and feature reduction via projection pursuit (1999) IEEE Trans. Geosci. Remote Sens, 37 (6), pp. 2653-2667. , Nov; 
Jimenez, L., Landgrebe, D., Supervised classification in high- dimensional space: Geometrical, statistical, and asymptotical properties of multivariate data (1998) IEEE Trans. Syst., Man, Cybern, 28 (1), pp. 39-54. , Pt. C, Feb; 
Marques deS{\'a}, J., (2001) Pattern Recognition - Concepts, Methods and Applications, , Berlin, Germany: Springer-Verlag; 
Heijden, F., Duin, R., Ridder, D., Tax, D., (2004) Classification, Parameter Estimation and State Estimation, , New York: Wiley; 
Dudoit, S., Fridlyand, J., Speed, T., Comparison of discrimination methods for the classification of tumors using gene expression data (2002) J. Amer. Stat. Assoc, 97, pp. 77-87; 
Zomer, S., Brereton, R., Carter, J., Eckers, C., Support vector machines for the discrimination of analytical chemical data: Application to the determination of tablet production by pyrolysis-gas chromatography-mass spectrometry (2004) Analyst, 129, pp. 175-181; 
Zomer, S., Guillo, C., Brereton, R., Hanna-Brown, M., Toxicological classification of urine samples using pattern recognition techniques and capilarity electrophoresis (2003) Anal. Bioanal. Chem, 378, pp. 2008-2020; 
Fan, X., Bai, J., Shen, P., Diagnosis of breast cancer using HPLC metabonomics fingerprints coupled with computational methods (2005) 27th Annu. Conf. Eng. Med. Biol, pp. 6081-6084. , Shanghai, China; 
Skurichina, M., Raudys, S., Duin, R., K-nearest neighbors directed noise injection in multilayer perceptron training (2000) IEEE Trans. Neural Netw, 11 (2), pp. 504-511. , Mar; 
Ha, T., Bunke, H., Off-line, handwritten numeral recognition by perturbation method (1997) IEEE Trans. Pattern Anal. Mach. Intell, 19, pp. 535-539; 
Lee, S., Noisy replication in skewed binary classification (2000) Comput. Stat. Data Anal, 34, pp. 165-191; 
Demant, C., Streicher-Abel, B., Waszkewitz, P., (1999) Industrial Image Processing, , New York: Springer-Verlag; 
Dempster, A., Laird, N., Rubin, D., Maximum likelihood from incomplete data via the EM algorithm (1977) J. Roy. Stat. Soc. B, 39 (1), pp. 1-38; 
Mor{\'e}, J., Sorensen, D., Computing a trust region step (1983) SIAM J. Sci. Stat. Comput, 4, pp. 553-572; 
Byrd, R., Schnabel, R., Shultz, G., Approximate solution of the trust region problem by minimization over two-dimensional subspaces (1988) Math. Program, 40, pp. 247-263; 
Sousa, A., Mendon{\c c}a, A., Campilho, A., Aguiar, R., Miranda, C., Feature extraction for classification of thin-layer chromatography images (2005) Lecture Notes in Computer Science, 3656, pp. 974-981. , Image Analysis and Recognition, New York: Springer-Verlag; 
Ripley, B., (2004) Pattern Recognition and Neural Networks, , New York: Cambridge Univ. Press; 
Visa, S., Ralescu, A., Learning imbalanced and overlapping classes using fuzzy sets (2003) ICML 2003, Workshop Learn. Imbalanced Datasets, 2. , presented at the, Washington, DC
},
	month = {2008///},
	pages = {1687 - 1696},
	abstract = {In this paper, we propose and evaluate methodologies for the classification of images from thin-layer chromatography. Each individual sample is characterized by an intensity profile that is further represented into a feature space. The first steps of this process aim at obtaining a robust estimate of the intensity profile by filtering noise, reducing the influence of background changes, and by fitting a mixture of Gaussians. The resulting profiles are represented by a set of appropriate features trying to characterize the state of nature, here spread out over four classes, one for normal subjects and the other three corresponding to lysosomal diseases, which are disorders responsible for severe nerve degeneration. For classification purposes, a novel solution based on a hierarchical structure is proposed. The main conclusion of this paper is that an automatically generated decision tree presents better results than more conventional solutions, being able to deal with the natural imbalance of the data that, as consequence of the rarity of lysosomal disorders, has very few representative cases in the disease classes when compared with the normal population. {\textcopyright} 2008 IEEE.},
	keywords = {Acoustic variables control, Algorithms, article, Chromatographic analysis, Chromatographic pattern features, Chromatographic profiles, Chromatography, Chromatography, Thin Layer, Classification, Diagnosis, Computer-Assisted, Gaussian noise (electronic), Hierarchical classifier, Humans, Hydrolases, imaging, Lysosomal Storage Diseases, lysosome storage disease, mathematical analysis, nerve degeneration, noise, normal distribution, Pattern recognition, Pattern Recognition, Automated, Reproducibility of Results, sensitivity and specificity, Urinalysis},
	isbn = {00189294 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-44349183146\&partnerID=40\&md5=df677f44d528c936310d8c547624a550},
	author = {Sousa, A.V. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@article {2340,
	title = {Dissimilarity-based classification of chromatographic profiles},
	journal = {Pattern Analysis and ApplicationsPattern Anal. Appl.},
	volume = {11},
	year = {2008},
	note = {Cited By (since 1996): 3Export Date: 21 November 2012Source: Scopusdoi: 10.1007/s10044-008-0113-2Language of Original Document: EnglishCorrespondence Address: Sousa, A. V.; Instituto Superior de Engenharia do Porto, Rua Dr. Ant{\'o}nio Bernardino Almeida, 431, 4200-072 Porto, Portugal; email: ats@isep.ipp.ptReferences: Reich, E., Blatter, A., Modern TLC: A key technique for identification and quality control of botanicals and dietary supplements (2004) Inside Laboratory Management-AOC International;Sousa, A.V., Aguiar, R., Mendon{\c c}a, A.M., Campilho, A., Automatic lane and band detection in images of thin layer chromatography (2004) LNCS, 3212, pp. 158-165. , Proceedings of image analysis and recognition: international conference, ICIAR 2004, Porto Springer, Heidelberg; 
Keir, G., Winchester, B.G., Clayton, P., Carbohydrate-deficient glycoprotein syndromes: Inborn errors of protein glycosylation (1999) Ann Clin Biochem, 36, pp. 20-36. , Pt 1; 
Durand, G., Seta, N., Protein glycosylation and diseases: Blood and urinary oligosaccharides as markers for diagnosis and therapeutic monitoring (2000) Clin Chem, 46, pp. 795-805; 
Schwedt, G., (1997) The Essential Guide to Analytical Chemistry, , Wiley New York; 
Bajla, I., Hollander, I., Fluch, S., Burg, K., Kollar, M., An alternative method for electrophoretic gel image analysis in the GelMaster software (2005) Comput Methods Programs Biomed, 77, pp. 209-231; 
Eibrand, R., Kennedy, P., Cotter, D., MacEvilly, U., Wu, B., Analysis of atlantic salmon skin mucus: COPS-a computer-based system for protein pattern analysis of 1D SDS-PAGE gels (2003) Proceedings of the Third IEEE Symposium on Bioinformatics and Bioengeneering; 
Gerasimov, A.V., Use of the software processing of scanned chromatogram images in quantitative planar chromatography (2004) J Anal Chem, 59, pp. 348-353; 
(2000) Gel Analysis Software: Important Aspects, , Goulding electrophoresis PN UVItec Ltd; 
Jedra, M., El Khattabi, N., Limouri, M., Essaid, A., Recognition of seed varieties using a time-delay neural network: Analysis of electrophoretic images (1999) Comput Electron Agric, 22, pp. 1-10; 
MacHado, A.M.C., Campos, M.F.M., Siqueira, A.M., De Carvalho, O.S.F., An iterative algorithm for segmenting lanes in gel electrophoresis images. Computer graphics and image processing (1997) Proceedings of X Brazilian Symposium, pp. 140-146; 
Ye, X., Suen, C.Y., Cheriet, M., Wang, E., A recent development in image analysis of electrophoresis gels (1999) Vision Iterface 99, , Trois-Rivieres, Canada; 
Wang, D., Keller, J.M., Carson, C.A., Pulsed-field gel electrophoresis pattern recognition of bacterial DNA: A systemic approach (2001) Pattern Anal Appl, 4, pp. 244-255; 
Shadle, S.E., Allen, D.F., Guo, H., Pogozelski, W.K., Bashkin, J.S., Tullius, T.D., Quantitative analysis of electrophoresis data: Novel curve fitting methodology and its application to the determination of a protein-NA binding constant (1997) Nucleic Acids Res, 25, pp. 850-860; 
Bajla, I., Hollander, I., Burg, K., Improvement of electrophoretic gel image analysis (2001) Measurement Science Review, 1; 
Bajla, I., Hollander, I., Burg, K., Fluch, S., (2002) A Novel Approach to Quantitative Analysis of Electrophoretic Gel Images of DNA Fragments, pp. 899-902. , IEEE international symposium on biomedical imaging Washington; 
Lavine, B.K., Meyers, R.A., Clustering and classification of analytical data (2000) Encyclopedia of Analytical Chemistry: Instrumentation and Applications, pp. 9689-9710. , Wiley Chichester; 
Lonni, A., Scarminio, I., Silva, L., Ferreira, D., Differentiation of species of bacharis genus by HPLC and chemometrics methods (2003) Analytical Sciences, 19; 
Beltr{\'a}n, N.H., Duarte-Mermoud, M.A., Salah, S.A., Bustos, M.A., Feature selection algorithms using Chilean wine chromatograms as examples (2005) J Food Eng, 67, pp. 483-490; 
Landgrebe, D., Information extraction principles and methods for multispectral and hyperspectral image data (1999) Proceedings of Information Processing for Remote Sensing, , World Scientific, Singapore; 
Landgrebe, D., Hyperspectral image data analysis as a high dimensional signal processing problem (2002) IEEE Signal Process Mag, 19, pp. 17-28; 
Jimenez, L., Landgrebe, D., Hyperspectral data analysis and feature reduction via projection pursuit (1999) IEEE Trans Geosci Remote Sens, 37, pp. 2653-2667; 
Jimenez, L.O., Landgrebe, D.A., Supervised classification in high-dimensional space: Geometrical, statistical, and asymptotical properties of multivariate data (1998) IEEE Transaction on Systems Man, and Cybernetics-Part C: Applications and Reviews, 28; 
Paclik, P., Duin, R.P.W., Dissimilarity-based classification of spectra: Computational issues (2003) Real-Time Imaging, 9, pp. 237-244; 
Paclik, P., Duin, R.P.W., Classifying spectral data using relational representation (2003) Proceedings of Spectral Imaging Workshop, , Graz; 
Pekalska, E., Paclik, P., Duin, R.P.W., A generalized kernel approach to dissimilarity-based classification (2001) J Mach Learn Res, 2, pp. 175-211; 
Pekalska, E., Duin, R.P.W., Dissimilarity representations allow for building good classifiers (2002) Pattern Recogn Lett, 23, pp. 943-956; 
Pekalska, E., Duin, R.P.W., (2005) The Dissimilarity Representation for Pattern Recognition-foundations and Applications, 64. , World Scientific, Singapore; 
Pekalska, E., Duin, R.P.W., Paclik, P., Prototype selection for dissimilarity-based classifiers (2006) Pattern Recogn, 39, pp. 189-208; 
Harol, A., Lai, C., Pekalska, E., Duin, R.P.W., Pairwise feature evaluation for constructing reduced representations (2007) Pattern Anal Appl, 10, pp. 55-68; 
Dubuisson, M.-P., Jain, A.K., A modified Hausdorff distance for object matching (1994) Proceedings of International Conference on Pattern Recognition, pp. 566-568. , Jerusalem; 
Heijden, F., Robert, P.W.D., Ridder, D., Tax, D.M.J., (2004) Classification, Parameter Estimation and State Estimation, , Wiley, New York; 
Dasarathy, B.V., S{\'a}nchez, J.S., Townsend, S., Nearest neighbour editing and condensing tools-synergy exploitation (2000) Pattern Anal Appl, 3, pp. 19-30; 
Devijver, P., Kittler, J., (1982) Pattern Recognition: A Statistical Approach, , Prentice-Hall, New Jersey; 
Cheng, Y., Mean shift, mode seeking, and clustering (1995) IEEE Trans Pattern Anal Mach Intell, 17, pp. 790-799; 
Vijaya, P.A., Murty, M.N., Subramanian, D.K., Efficient median based clustering and classification techniques for protein sequences (2006) Pattern Anal Appl, 9, pp. 243-255; 
Sousa, A.V., Mendon{\c c}a, A.M., Campilho, A., Aguiar, R., Miranda, C.S., Feature extraction for classification of thin-layer chromatography images (2005) Proceedings of Image Analysis and Recognition, Second International Conference, ICIAR 2005, 3656, pp. 974-981. , LNCS Springer, Toronto; 
Dempster, A., Laird, N., Rubin, D., Maximum likelihood from incomplete data via the em algorithm (1977) J R Stat Soc, B-39, pp. 1-38. , 1; 
Mor{\'e}, J.J., Sorensen, D.C., Computing a trust region step (1983) SIAM J Sci Stat Comput, 4, pp. 553-572; 
Byrd, R.H., Schnabel, R.B., Shultz, G.A., Approximate solution of the trust region problem by minimization over two-dimensional subspaces (1988) Math Program, 40, pp. 247-263; 
Visa, S., Ralescu, A., Learning imbalanced and overlapping classes using fuzzy sets (2003) Workshop on Learning from Imbalanced Datasets II, , ICML, Washington DC; 
Sousa, A.V., Mendon{\c c}a, A.M., Campilho, A.C., The class imbalance problem in TLC image classification (2006) Proceedings of Image Analysis and Recognition, Third International Conference, ICIAR 2006, 4142, pp. 513-523. , September 18-20, 2006, proceedings, Part II, P{\'o}voa de Varzim. LNCS Springer, Portugal
},
	month = {2008///},
	pages = {409 - 423},
	abstract = {This paper proposes a non-parametric method for the classification of thin-layer chromatographic (TLC) images from patterns represented in a dissimilarity space. Each pattern corresponds to a mixture of Gaussian approximation of the intensity profile. The methodology comprises various phases, including image processing and analysis steps to extract the chromatographic profiles and a classification phase to discriminate among two groups, one corresponding to normal cases and the other to three pathological classes. We present an extensive study of several dissimilarity-based approaches analysing the influence of the dissimilarity measure and the prototype selection method on the classification performance. The main conclusions of this paper are that, Match and Profile-difference dissimilarity measures present better results, and a new prototype selection methodology achieves a performance similar or even better than conventional methods. Furthermore, we also concluded that simplest classifiers, such as k-NN and linear discriminant classifiers (LDCs), present good performance being the overall classification error less than 10\% for the four-class problem. {\textcopyright} 2008 Springer-Verlag London Limited.},
	keywords = {Chromatographic images, Dissimilarity representation, Nearest neighbour classifiers, Prototype selection},
	isbn = {14337541 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-50549105086\&partnerID=40\&md5=2a5bd02f0caacb5e7ccb1267a861db75},
	author = {Sousa, A.V. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@book {2343,
	title = {Minimizing the imbalance problem in chromatographic profile classification with one-class classifiers},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {5112 LNCS},
	year = {2008},
	note = {Conference code: 72695Cited By (since 1996): 2Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-540-69812-8_41Language of Original Document: EnglishCorrespondence Address: Sousa, A. V.; Instituto de Engenharia Biom{\'e}dica, Rua Roberto Frias, 4200-465 Porto, Portugal; email: ats@isep.ipp.ptReferences: Keir, G., Winchester, B.G., Clayton, P., Carbohydrate-deficient Glycoprotein Syndromes: Inborn Errors of Protein Glycosylate (1999) Ann. Clin. Biochem, 36 (PART 1), pp. 20-36;Durand, G., Seta, N., Protein Glycosylation and Diseases: Blood and Urinary Oligosaccharides as Markers for Diagnosis and Therapeutic Monitoring (2000) Clin. Chem, 46, pp. 795-805; 
Scriver, S.R., Beaudet, A.L., Sly, W.S., Valle, D., (2001) The Metabolic Bases of Inherited Disease, 3. , MCGraw-Hill, New-York; 
Friedman, R.B., Williams, M.A., Moser, H.W., Kolodny, E.H., Improved Thin-Layer Chromatographic Method in the Diagnosis of Mannosidosis (1978) Clinical Chemistry, 24, pp. 1576-1577; 
Kurczynski, T.W., Kendzierski, K.S., Sewell, A.C., Kuczynski, T.W., Urinary Oligosaccharides in Pregnant or Lactating Women: Pitfall in Screening (1993) Clin. Chem, 39, pp. 2346-2347; 
Sewell, A.C., Urinary Oligosaccharides. Techniques in Diagnostic Human (1991) Biochemical Genetics, 219-231; 
Duvdevani-Bar, S., Edelman, S., Visual Recognition and Categorization on the Basis of Similarities to Multiple Class Prototypes (1999) Int. J. Comput. Vision, 33, pp. 201-228; 
Pekalska, E., Duin, R.P.W., Dissimilarity Representations Allow for Building Good Classifiers (2002) Pattern Recogn. Lett, 23, pp. 943-956; 
Goldstone, R.L., Son, J.Y., Similarity (2005) Cambridge Handbook of Thinking and Reasoning, pp. 13-36. , Holyoak, K, Morrison, R, eds, Cambridge University Press, Cambridge; 
Pekalska, E., Duin, R.P.W., (2005) The Dissimilarity Representation for Pattern Recognition -Fundations and Applications, 64. , World Scientific Publishing, Singapore; 
Lavine, B.K., Clustering and Classification of Analytical Data (2000) Encyclopedia of Analytical Chemistry: Instrumentation and Applications, pp. 9689-9710. , Meyers, R.A, ed, John Wiley \& Sons, Chichester; 
Kubat, M., Holte, R., Matwin, S.: Learning when Negative Examples Abound. In: van Someren, M., Widmer, G. (eds.) ECML 1997. LNCS, 1224, pp. 146-153. Springer, Heidelberg (1997)Skurichina, M., Raudys, S., Duin, R.P.W., K-Nearest Neighbors Directed Noise Injection in Multilayer Perceptron Training (2000) IEEE Transactions on Neural Networks, 11; 
Chawla, N.V., Japkowicz, N., Kolcz, A., Editorial: Special Issue: Learning from Imbalanced Data Sets (2004) Sigkdd Explorations, 6, pp. 1-6; 
Tax, D.M.J., (2001) One-class Classification; Concept-learning in the Absence of Counter-examples, , Ph.D. thesis: Delft University of Technology; 
Sousa, A.V., Mendon{\c c}a, A.M., Campilho, A., Chromatographic Pattern Classification (2007) IEEE Trans. on Biomedical Engineering, , doi:10.1109/TBME.2008.919852; 
Paclik, P., Duin, R.P.W., Dissimilarity-based Classification of Spectra: Computational Issues (2003) Real-Time Imaging, 9, pp. 237-244; 
Paclik, P., Duin, R.P.W., Classifying Spectral Data using Relational Representation (2003) Proc. of Spectral Imaging Workshop, , Graz, Austria; 
Sousa, A.V., Mendon{\c c}a, A.M., Campilho, A., Dissimilarity-based Classification of Chromatographic Profiles (2007) Pattern Analysis and Applications, , doi:10.1007/sl0044-008-0113-2; 
Pekalska, E., Paclik, P., Duin, R.P.W., A Generalized Kernel Approach to Dissimilarity-based Classifcation (2001) Journal of Machine Learning Research, 2, pp. 175-211; 
Ripley, B.D., (2004) Pattern Recognition and Neural Networks, , Cambridge University Press, Cambridge; 
Visa, S., Ralescu, A., Learning Imbalanced and Overlapping Classes Using Fuzzy Sets (2003) Workshop on Learning from Imbalanced Datasets II, ICML, , Washington DC
},
	month = {2008///},
	pages = {413 - 422},
	address = {Povoa de Varzim},
	abstract = {This paper presents a new classification approach to deal with class imbalance in TLC patterns, which is due to the huge difference between the number of normal and pathological cases as a consequence of the rarity of LSD diseases. The proposed architecture is formed by two decision stages: the first is implemented by a one-class classifier aiming at recognizing most of the normal samples; the second stage is a hierarchical classifier which deals with the remaining outliers that are expected to contain the pathological cases and a small percentage of normal samples. We have also evaluated this architecture by a forest of classifiers, using the majority voting as a rule to generate the final classification. The results that were obtained proved that this approach is able to overcome some of the difficulties associated with class imbalance. {\textcopyright} 2008 Springer-Verlag Berlin Heidelberg.},
	keywords = {Class imbalances, Classification (of information), Classification approach, Classifiers, Heidelberg (CO), Hierarchical classifiers, Image analysis, Imaging techniques, Imbalance problems, International conferences, Learning systems, Majority voting, Pathological cases},
	isbn = {03029743 (ISSN); 3540698116 (ISBN); 9783540698111 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-47849088119\&partnerID=40\&md5=df911723c51c6f540287fdd675267725},
	author = {Sousa, A.V. and Mendon{\c c}a, A.M. and Campilho, A.}
}
@book {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 {2439,
	title = {Feature extraction for classification of thin-layer chromatography images},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {3656 LNCS},
	year = {2005},
	note = {Conference code: 67124Cited By (since 1996): 2Export Date: 21 November 2012Source: Scopusdoi: 10.1007/11559573_118Language of Original Document: EnglishCorrespondence Address: Sousa, A.V.; Instituto de Engenharia Biom{\'e}dica, Rua Roberto Frias, 4200-465 Porto, Portugal; email: ats@isep.ipp.ptReferences: Bajla, I., Holl{\"a}nder, I., Burg, K., Improvement of electrophoretic gel image analysis (2001) M. S.R, 1 (1);Ye, X., Suen, C., Cheriet, M., Wang, E., A recent development in image analysis of electrophoresis gels (1999) Vision Interface 99, , Canada; 
Shadle, S., Allen, D., Guo, H., Pogozelski, W., Bashkin, J., Tullius, T., Quantitative analysis of electrophoresis data: Novel curve fitting methodology and its application to the determination of aprotein-DNA binding constant (1997) Nucleic Acids Research, 25 (4); 
Sousa, A., Aguiar, R., Mendon{\c c}a, A.M., Campilho, A., Automatic lane and band detection in images of thin layer chromatography (2004) Springer Lecture Notes on Computer Science, LNCS, 3212, pp. 372-380; 
Heijden, F., Duin, R., Ridder, D., Tax, D., (2004) Classification, Parameter Estimation and State Estimation, , John Wiley and Sons, England; 
Sewell, A., (1991) Urinary Oligosaccharides. Techniques in Diagnostic Human Biochemical Genetics, pp. 219-231. , Willey-Liss, Inc; 
Thomas, G., Disorders of Glycoprotein Degradation. α-Mahnosidosis, β-Mannosidosis, Fucosidosis and Sialidosis. Disorders of Glycoprotein Degradation, , Chap. 140; 
Durant, G., Seta, N., Protein glycosylation and diseases: Blood and urinary oligosaccharides as markers for diagnosis and therapeutic monitoring (2000) Clinical Chemistry, 46 (6), pp. 795-805; 
Marques De S{\'a}, J., (2003) Applied Statistics Using SPSS, STATISTICA and MATLAD, , Springer Verlag
},
	month = {2005///},
	pages = {974 - 981},
	address = {Toronto},
	abstract = {Thin-Layer Chromatography images are used to detect and identify the presence of specific oligosaccharides, expressed by the existence, at different positions, of bands in the gel image. ID gaussian deconvolution, commonly used for band detection, does not produce good results due to the large curvature observed in the bands. To overcome this uncertainty on the band position, we propose a novel feature extraction methodology that allows an accurate modeling of curved bands. The features are used to classify the data into two different classes, to differentiate normal from pathologic cases. The paper presents the developed methodology together with the analysis and discussion of the results. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005.},
	keywords = {Edge detection, Feature extraction, Image analysis, Image segmentation, Oligosaccharides, Thin layer chromatography, Thin-Layer Chromatography images},
	isbn = {03029743 (ISSN); 3540290699 (ISBN); 9783540290698 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33645996661\&partnerID=40\&md5=6ef97f4dfec52add55258aec9e98bb52},
	author = {Sousa, A.V. and Mendon{\c c}a, A.M. and Campilho, A. and Aguiar, R. and S{\'a} Miranda, C.}
}
@book {2465,
	title = {Automatic lane and band detection in images of thin layer chromatography},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {3212},
	year = {2004},
	note = {Cited By (since 1996): 7Export Date: 21 November 2012Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Sousa, A.V.; Institute de Engenharia Biom{\'e}dica, Rua Roberto Prias, 4200-465 Porto, Portugal; email: ats@isep.ipp.ptReferences: Sewell, A., (1991) Urinary Oligosaccharides. Techniques in Diagnostic Human Biochemical Genetics, pp. 219-231. , Willey-Liss, Inc;Thomas, G., Disorders of Glycoprotein Degradation. α-Mannosidosis, β-Mannosidosis, Fucosidosis and Sialidosis, , Disorders of Glycoprotein Degradation (Chap. 140); 
Starr, C., Masada, R., Hague, C., Klock, J., Fluorophore-assisted Carbohydrate Electrophoresis in the Separation, Analysis, and Sequencing of Carbohydrates (1996) Journal of Chromatography A, 720, pp. 295-321; 
Klein, A., Lebreton, A., Lemoine, J., P{\'e}rini, J., Roussel, P., Michalski, J., Identification of Urinary Oligosaccharides by Matrix-assisted Laser Desorption Ionization Time-of-flight Mass Spectrometry (1998) Clinical Chemistry, 44 (12), pp. 2422-2428; 
Dempster, A., Laird, N., Rubin, D., Maximum Likelihood from Incomplete Data via the em Algorithm (1977) Journal of the Royal Statistical Society, B, 39 (1), pp. 1-38; 
Schwedt, G., The Essential Guide to Analytical Chemistry, , John Wiley \& Sons; 
Dennis, J., Nonlinear Least-squares, State of the Art in Numerical Analysis, , D. Jacobs Academic Press; 
Byrd, R., Schnabel, R., Shultz, G., Approximate Solution of the Trust Region Problem by Minimization over Two-Dimensional Subspaces (1988) Mathematical Programming, 40, pp. 247-263; 
Branch, M., Coleman, T., Li, Y., A Subspace, Interior, and Conjugate Gradient Method for Large-Scale Bound-Constrained Minimization Problems (1999) SIAM SISC, 21 (1), pp. 1-23; 
Nabney, I., Netlab Algorithms for Pattern Recognition (2003) Advances in Pattern Recognition, , Springer; 
Marques, J., Reconhecimento de Padr{\~o}es, , M{\'e}todos Estatisticos e Neuronais, IST-Press; 
Machado, A., Campos, M., Siqueira, A., Carvalho, O., (1997) An Iterative Algorithm for Segmenting Lanes in Gel Electrophoresis Images; 
},
	month = {2004///},
	pages = {158 - 165},
	abstract = {This work aims at developing an automatic method for the analysis of TLC images for measuring a set of features that can be used for the characterization of the distinctive patterns that result from the separation of oligosaccharides contained in human urine. This paper describes the methods developed for the automatic detection of the lanes contained in TLC images, and for the automatic separation of bands for each detected lane. The extraction of quantitative information related with each band was accomplished with two methods: the EM expectation-maximization and nonlinear least squares trustregion algorithms. The results of these methods, as well as additional quantitative information related with each band, are also presented. {\textcopyright} Springer-Verlag 2004.},
	isbn = {03029743 (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33645961076\&partnerID=40\&md5=ef452eea389056fa97b81d62d8f451b9},
	author = {Sousa, A.V. and Aguiar, R. and Mendonca, A.M. and Campilho, A.}
}
