



@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; 
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},
	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.}
}
