Automatic detection of body parts in x-ray images

The number of digital images that needs to be acquired, analyzed, classified, stored and retrieved in the medical centers is exponentially growing with the advances in medical imaging technology. Accordingly, medical image classification and retrieval has become a popular topic in the recent years. Despite many projects focusing on this problem, proposed solutions are still far from being sufficiently accurate for real-life implementations. Interpreting medical image classification and retrieval as a multi-class classification task, in this work, we investigate the performance of five different feature types in a SVM-based learning framework for classification of human body X-Ray images into classes corresponding to body parts. Our comprehensive experiments show that four conventional feature types provide performances comparable to the literature with low per-class accuracies, whereas local binary patterns produce not only very good global accuracy but also good class-specific accuracies with respect to the features used in the literature.