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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Medical Image Classification at Tel Aviv and Bar Ilan Universities
verfasst von : Uri Avni, Jacob Goldberger, Hayit Greenspan
Erschienen in: ImageCLEF
Verlag: Springer Berlin Heidelberg
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We present an efficient and accurate image categorization system, applied to medical image databases within the ImageCLEF medical annotation task. The methodology is based on local representation of the image content, using a bag–of–visual–words approach. We explore the effect of different parameters on system performance, and show best results using dense sampling of simple features with spatial content in multiple scales, combined with a nonlinear kernel based Support Vector Machine classifier. The system was ranked first in the ImageCLEF 2009 medical annotation challenge, with a total error score of 852.8.