Automated chest X-ray image view classification using force histogram

Abstract

To advance and/or ease computer aided diagnosis (CAD) system, chest X-ray (CXR) image view information is required. In other words, separating CXR image view: frontal and lateral can be considered as a crucial step to effective subsequent processes, since the techniques that work for frontal CXRs may not equally work for lateral ones. With this motivation, in this paper, we present a novel machine learning technique to classify frontal and lateral CXR images, where we introduce a force histogram to extract features and apply three different state-of-theart classifiers: support vector machine (SVM), random forest (RF) and multi-layer perceptron (MLP). We validated our fully automatic technique on a set of 8100 images hosted by National Library of Medicine (NLM), National Institutes of Health (NIH), and achieved an accuracy close to 100%.