Difference between revisions of "Publications:Image analysis based categorization of laryngeal diseases"
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| − | |Name=Valincius, Donatas (Kaunas University of Technology);Verikas, Antanas | + | |Name=Valincius, Donatas (Kaunas University of Technology);Verikas, Antanas (av) (0000-0003-2185-8973) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), Intelligenta system (IS-lab) (3941));Gelzinis, Adas (Kaunas University of Technology);Bacauskiene, Marija (Kaunas University of Technology) |
|Title=Image analysis based categorization of laryngeal diseases | |Title=Image analysis based categorization of laryngeal diseases | ||
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Latest revision as of 21:41, 30 September 2016
| Title | Image analysis based categorization of laryngeal diseases |
|---|---|
| Author | Donatas Valincius and Antanas Verikas and Adas Gelzinis and Marija Bacauskiene |
| Year | 2006 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | Proceedings of the 1st International Conference on Electrical and Control Technologies, 2006 |
| Conference | 1st International Conference on Electrical and Control Technologies, 2006, MAY 04-05, 2006 Kaunas, LITHUANIA, 2006 |
| DOI | |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:577047 |
| Abstract | This paper concentrates on an automated analysis of laryngeal images aiming to categorize the images into three decision classes, namely healthy, nodular and diffuse. The problem is treated as an amage analysis and classification task. To obtain a comprehensive description of laryngeal images, multiple feature sets exploiting information on image colour, texture, geometry, image intensity gradient direction, and frequency content are extracted. A separate support vector machine (SVM) is used to categorize features of each type into decision classes. The final image categorization is then obtained which is based on the decisions provided by a committee of support vector machines. Bearing in mind a high similarity of the decision classes, the correct classification rate of over 94 % is obtained while testing the system on 785 laryngeal images that are recorded by the Department of Otolaryngology, Kaunas University of Medicine is rather promising. |
