Symbolic representation and classification of medical X-ray images

Abstract

In this paper, we propose a symbolic approach for classification of medical X-ray images. Graph cut segmentation is applied to segment the body part of medical X-ray images. A complete directed graph is constructed using the centroid points in the boundary image of the segmented body part image. The complete directed graph is in turn used to extract features of distance and orientation. Further, the boundaries of segmented images are represented by its skeleton end points. Shape features are then extracted from the represented skeleton end points. To assimilate feature variations, we propose to symbolically represent the extracted features of each class in the form of interval valued features. Based on the proposed symbolic representation, symbolic classifier is then used for classification of medical X-ray images. Experimental results reveal the efficiency of our proposed symbolic classification model.