Dense simple features for fast and accurate medical X-ray annotation

Authors:
Uri Avni;Hayit Greenspan;Jacob Goldberger
Affiliations:
BioMedical Engineering, Tel-Aviv University;BioMedical Engineering, Tel-Aviv University;Engineering School, Bar-Ilan University
Venue:
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Year:
2009

Citing 7
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Abstract

We present a simple, fast and accurate image categorization system, applied to medical image databases within the ImageCLEF 2009 medical annotation task. The methodology presented is based on local representation of the image content, using a bag of visual words approach in multiple scales, with a kernel based SVM classifier. The system was ranked first in this challenge, with total error score of 852.8.