Edge map analysis in chest X-rays for automatic pulmonary abnormality screening.

KC S, Vajda S, Antani S, Thoma GR

Int J Comput Assist Radiol Surg. 2016 Sep;11(9):1637-46. doi: 10.1007/s11548-016-1359-6. Epub 2016 Mar 19.

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Abstract:

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.

KC S, Vajda S, Antani S, Thoma GR. Edge map analysis in chest X-rays for automatic pulmonary abnormality screening. 
Int J Comput Assist Radiol Surg. 2016 Sep;11(9):1637-46. doi: 10.1007/s11548-016-1359-6. Epub 2016 Mar 19.

PMID