



@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.}
}
