Edge map analysis in chest X-rays for automatic pulmonary abnormality screening
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Our particular motivator is the need for screening HIV+ populations in resource-constrained regions for the evidence of tuberculosis, using posteroanterior chest radiographs (CXRs).
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 \([0, 2\pi )\) at different numbers of bins and different pyramid levels, using five different regions-of-interest selection.
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.
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.
KeywordsAutomation Thoracic edge map Pulmonary abnormalities Screening Tuberculosis Chest X-rays
This research was supported by the Intramural Research Program of the National Institutes of Health (NIH), National Library of Medicine (NLM) and Lister Hill National Center for Biomedical Communications (LHNCBC).
Compliance with ethical standards
Conflict of interest
K.C. Santosh, Szilárd Vajda, Sameer Antani and George R Thoma declare that they have no conflicts of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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