X-Ray Image Classification and Retrieval Using Ensemble Combination of Visual Descriptors

Authors:
Jeonghee Shim;Kihee Park;Byoungchul Ko;Jaeyeal Nam
Affiliations:
Dept of Computer Engineering, Keimyung University, Daegu, Korea 704-701;Dept of Computer Engineering, Keimyung University, Daegu, Korea 704-701;Dept of Computer Engineering, Keimyung University, Daegu, Korea 704-701;Dept of Computer Engineering, Keimyung University, Daegu, Korea 704-701
Venue:
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Year:
2009

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Abstract

In this paper, we propose a novel algorithm for the efficient classification and retrieval of medical images, especially X-ray images. Since medical images have bright foreground against dark background, we extract MPEG-7 visual descriptor from only salient parts of foreground. For color descriptor, Color Structure Descriptor (H-CSD) is extracted from salient points, which are detected by Harris corner detector. For texture descriptor, Edge Histogram Descriptor (EHD) is extracted from global and local parts of images. Then extracted feature vector is applied to multi-class Support Vector Machine (SVM) to give membership scores for each image. From the membership scores of H-CSD and EHD, two membership scores are combined as one ensemble feature and it is used for similarity matching of our retrieval system, MISS (Medical Information Searching System). The experimental results using CLEF-Med2007 images show that our system can indeed improve retrieval performance compared to other global property-based or other classification-based retrieval methods.