Publications:A kernel-based approach to categorizing laryngeal images


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Title A kernel-based approach to categorizing laryngeal images
Author Antanas Verikas and Adas Gelzinis and Marija Bacauskiene and Donata Valincius and Virgilijus Uloza
Year 2007
PublicationType Journal Paper
Journal Computerized Medical Imaging and Graphics
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Abstract This paper is concerned with an approach to automated analysis of vocal fold images aiming to categorize laryngeal diseases. Colour, texture, and geometrical features are used to extract relevant information. A committee of support vector machines is then employed for performing the categorization of vocal fold images into healthy, diffuse, and nodular classes. The discrimination power of both, the original and the space obtained based on the kernel principal component analysis is investigated. A correct classification rate of over 92% was obtained when testing the system on 785 vocal fold images. Bearing in mind the high similarity of the decision classes, the correct classification rate obtained is rather encouraging.