Publications:Categorizing sequences of laryngeal data for decision support


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Title Categorizing sequences of laryngeal data for decision support
Author Adas Gelzinis and Evaldas Vaiciukynas and Edgaras Kelertas and Marija Bacauskiene and Antanas Verikas and Virgilijus Uloza and Aurelija Vegiene
Year 2009
PublicationType Conference Paper
HostPublication ECT 2009 : Electrical and Control Technologies
Conference 4th International Conference on Electrical and Control Technologies, ECT 2009, Kaunas University of Technology, Kaunas, Lithuania, 7-8 May 2009
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Abstract This paper is concerned with kernel-based techniques for categorizing laryngeal disorders based on information extracted from sequences of laryngeal colour images. The features used to characterize a laryngeal image are given by the kernel principal components computed using the N-vector of the 3-D colour histogram. The least squares support vector machine (LS-SVM) is designed for categorizing an image sequence into the healthy, nodular and diffuse classes. The kernel function employed by the SVM classifier is defined over a pair of matrices, rather than over a pair of vectors. An encouraging classification performance was obtained when testing the developed tools on data recorded during routine laryngeal videostroboscopy.