Medical Image Automatic Annotation Using Multi-class SVM and Annotation Code Array 


Vol. 16,  No. 4, pp. 281-288, Aug.  2009
10.3745/KIPSTB.2009.16.4.281


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  Abstract

This paper proposes a novel algorithm for the efficient classification and annotation of medical images, especially X-ray images. Since X-ray images have a bright foreground against a dark background, we need to extract the different visual descriptors compare with general nature images. In this paper, a Color Structure Descriptor (CSD) based on Harris Corner Detector is only extracted from salient points, and an Edge Histogram Descriptor (EHD) used for a textual feature of image. These two feature vectors are then applied to a multi-class Support Vector Machine (SVM), respectively, to classify images into one of 20 categories. Finally, an image has the Annotation Code Array based on the pre-defined hierarchical relations of categories and priority code order, which is given the several optimal keywords by the Annotation Code Array. Our experiments show that our annotation results have better annotation performance when compared to other method.

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[IEEE Style]

K. H. Park, B. C. Ko, J. Y. Nam, "Medical Image Automatic Annotation Using Multi-class SVM and Annotation Code Array," The KIPS Transactions:PartB , vol. 16, no. 4, pp. 281-288, 2009. DOI: 10.3745/KIPSTB.2009.16.4.281.

[ACM Style]

Ki Hee Park, Byoung Chul Ko, and Jae Yeal Nam. 2009. Medical Image Automatic Annotation Using Multi-class SVM and Annotation Code Array. The KIPS Transactions:PartB , 16, 4, (2009), 281-288. DOI: 10.3745/KIPSTB.2009.16.4.281.

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