Combining Multilevel Visual Features for Medical Image Retrieval in ImageCLEFmed 2005

In this paper we report our work on the fully automatic medical image retrieval task in ImageCLEFmed 2005. First, we manually identify visually similar sample images by visual perception for each query topic. These help us understand the variations of the query topic and form templates for similarity measure. To achieve higher performance, two similarity measuring channels are used with each using difierent sets of features and operating in parallel. Their results are then combined to form a flnal score for similarity ranking. To improve e‐ciency, a pre-flltering process using other features is utilized to act as a coarse topic image flltering before the two similarity measures for flne topic retrieval. During retrieval, no relevance feedback is used. Only visual features are used in our experiments for all the topics including visually possible queries (topics 1{11), mixed visual/semantic queries (topics 12-22) and semantic (rather textual) queries (topics 23-25). Over 50,000 medical images our approach achieved a mean average precision of 14.6% for all 25 topics, ranked as the best-performance run for the automatic medical image retrieval task in the ImageCLEFmed 2005.

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