Automatic Pulmonary Abnormality Screening Using Thoracic Edge Map

We present a novel method for screening pulmonary abnormalities using thoracic edge map in PA chest radiograph (CXR) images. Our particular interest is to aid clinical officers in screening HIV+ populations in resource constrained regions for Tuberculosis (TB). Our work is motivated by the observation that abnormal CXRs tend to exhibit corrupted and/or deformed thoracic edge maps. We study histograms of thoracic edges for all possible orientations of gradients in the range [0, 2π) at different numbers of bins and different pyramid levels. We have used two CXR benchmark collections made available by the U.S. National Library of Medicine, and have achieved a maximum abnormality detection accuracy of 85.92% and area under the ROC curve (AUC) of 0.91 at one second per image, on average, which outperforms the reported state-of-the-art.