Supervised Machine Learning Based Medical Image Annotation and Retrieval in ImageCLEFmed 2005

Abstract

This paper presents the methods and experimental results for the automatic medical image annotation and retrieval task of ImageCLEFmed 2005. A supervised machine learning approach to associate low-level image features with their high level visual and/or semantic categories is investigated. For automatic image annotation, the input images are presented as a combined feature vector of texture, edge and shape features. A multi-class classifier based on pairwise coupling of several binary support vector machine is trained on these inputs to predict the categories of test images. For visual only retrieval, a combined feature vector of color, texture and edge features is utilized in low dimensional PCA sub-space. Based on the online category prediction of query and database images by the classifier, pre-computed category specific first and second order statistical parameters are utilized in a Bhattacharyya distance measure. Experimental results of both image annotation and retrieval are reported in this paper.