Medical Image Classification at Tel Aviv and Bar Ilan Universities

Uri Avni, Jacob Goldberger, Hayit Greenspan

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


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.
Original languageUndefined/Unknown
Title of host publicationImageCLEF: Experimental Evaluation in Visual Information Retrieval
EditorsHenning Müller, Paul Clough, Thomas Deselaers, Barbara Caputo
Place of PublicationBerlin, Heidelberg
PublisherSpringer Berlin Heidelberg
Number of pages17
ISBN (Print)978-3-642-15181-1
StatePublished - 2010

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