Classification of medical images using edge-based features and sparse representation

In this paper, an approach for classification of medical images using edge-based features is proposed. We demonstrate that the edge information extracted from an image by dividing the image into patches and each patch into concentric circular regions provide discriminative information useful for classification of medical images by considering 18 categories of radiological medical images namely, skull, hand, breast, cranium, hip, cervical spin, pelvis, radiocarpaljoint, elbow etc.,. The ability of On-line Dictionary Learning (ODL) to achieve sparse representation of an image is exploited to develop dictionaries for each class using edge-based feature. A low rate of misclassification error for these test images validates the effectiveness of edge-based features and On-line Dictionary Learning models for classification of medical images.