Purpose: Our particular motivator is the need for screening HIV+ populations in resource-constrained regions for the evidence of tuberculosis, using posteroanterior chest radiographs (CXRs).
Method: 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 [Formula: see text] at different numbers of bins and different pyramid levels, using five different regions-of-interest selection.
Results: 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 1 s per image, on average.
Conclusion: We have presented an automatic method for screening pulmonary abnormalities using thoracic edge map in CXR images. The proposed method outperforms previously reported state-of-the-art results.
Keywords: Automation; Chest X-rays; Pulmonary abnormalities; Screening; Thoracic edge map; Tuberculosis.