Sixth International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2009)
This paper is concerned with kernel-based techniques for automated categorization of laryngeal colour image sequences obtained by video laryngostroboscopy. 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 (video) into the healthy, cancerous and noncancerous classes. The kernel function employed by the LS-SVM is defined over a pair of matrices, rather than over a pair of vectors. The classification accuracy of over 85% was obtained when testing the developed tools on data recorded during routine laryngeal videostroboscopy.
Index Terms. Larynx pathology, Image sequence, Classification, Support vector machine
Full Paper (reprinted with permission from Firenze University Press)
Bibliographic reference. Gelzinis, A. / Verikas, Antanas / Bacauskiene, M. / Vaiciukynas, E. / Kelertas, E. / Uloza, V. / Vegiene, A. (2009): "Towards video laryngostroboscopy-based automated screening for laryngeal disorders", In MAVEBA-2009, 125-128.