Medical Image Categorization using a Texture Based Symbolic Description

In the field of medical image indexation, automatic categorization provides the means for extracting, otherwise unavailable, information from images. Our work is focused on content-based automatic medical image categorization methods, in the on-line context of the CISMeF health-catalogue. In this study we propose and evaluate a reduced symbolic image representation. The categorization of medical images according to their modality, anatomic region and view angle is based on texture and statistical features. We use a medical image dataset of 10322 images, representing 33 classes, manually annotated by an experienced radiologist. A top classification accuracy of 92.43% is obtained using k-Nearest Neighbors classifier on a 64-label symbolic representation. This shows that the compact symbolic image representation we propose conveys enough of the initial texture information to obtain high recognition rates, despite the complex context of multi-modal medical image categorization.