



@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.}
}
