Difference between revisions of "Publications:Integrating global and local analysis of color, texture and geometrical information for categorizing laryngeal images"
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| − | |Name=Verikas, Antanas | + | |Name=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));Gelzinis, Adas (Department of Applied Electronics, Kaunas University of Technology, Lithuania);Bacauskiene, Marija (Department of Applied Electronics, Kaunas University of Technology, Lithuania);Uloza, Virgilijus (Department of Otolaryngology, Kaunas University of Medicine, Lithuania) |
|Title=Integrating global and local analysis of color, texture and geometrical information for categorizing laryngeal images | |Title=Integrating global and local analysis of color, texture and geometrical information for categorizing laryngeal images | ||
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| Title | Integrating global and local analysis of color, texture and geometrical information for categorizing laryngeal images |
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| Author | |
| Year | 2006 |
| PublicationType | Journal Paper |
| Journal | International journal of pattern recognition and artificial intelligence |
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| Conference | |
| DOI | http://dx.doi.org/10.1142/S0218001406005228 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:286852 |
| Abstract | An approach to integrating the global and local kernel-based automated analysis of vocal fold images aiming to categorize laryngeal diseases is presented in this paper. The problem is treated as an image analysis and recognition task. A committee of support vector machines is employed for performing the categorization of vocal fold images into healthy, diffuse and nodular classes. Analysis of image color distribution, Gabor filtering, cooccurrence matrices, analysis of color edges, image segmentation into homogeneous regions from the image color, texture and geometry view point, analysis of the soft membership of the regions in the decision classes, the kernel principal components based feature extraction are the techniques employed for the global and local analysis of laryngeal images. Bearing in mind the high similarity of the decision classes, the correct classification rate of over 94% obtained when testing the system on 785 vocal fold images is rather encouraging. |
