NIH Research Festival
We present a novel method for automatic screening pulmonary abnormalities using thoracic edge map in posteroanterior chest radiograph (CXR) images. Our particular motivator is the need for screening HIV+ populations in resource constrained regions for Tuberculosis (TB). The proposed method 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 (ACC) of 86.36% and area under the ROC curve (AUC) of 0.93 at one second per image, on average, which outperforms the reported state-of-the-art.
Scientific Focus Area: Computational Biology
This page was last updated on Friday, March 26, 2021