



@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.}
}
