Medical image retrieval using texture, locality and colour

Authors:
Peter Howarth;Alexei Yavlinsky;Daniel Heesch;Stefan Rüger
Affiliations:
Multimedia Information Retrieval, Department of Computing, Imperial College London, UK;Multimedia Information Retrieval, Department of Computing, Imperial College London, UK;Multimedia Information Retrieval, Department of Computing, Imperial College London, UK;Multimedia Information Retrieval, Department of Computing, Imperial College London, UK
Venue:
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
Year:
2004

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Abstract

We describe our experiments for the Image CLEF medical retrieval task. Our efforts were focused on the initial visual search. A content-based approach was followed. We used texture, localisation and colour features that have been proven by previous experiments. The images in the collection had specific characteristics. Medical images have a formulaic composition for each modality and anatomic region. We were able to choose features that would perform well in this domain. Tiling a Gabor texture feature to add localisation information proved to be particularly effective. The distances from each feature were combined with equal weighting. This smoothed the performance across the queries. The retrieval results showed that this simple approach was successful, with our system coming third in the automatic retrieval task.