Edge map analysis in chest X-rays for automatic pulmonary abnormality screening
Department or Administrative Unit
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[0,2π)" role="presentation">[0,2π)
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.
Santosh, K. C., Vajda, S., Antani, S., & Thoma, G. R. (2016). Edge map analysis in chest X-rays for automatic pulmonary abnormality screening. International Journal of Computer Assisted Radiology and Surgery, 11(9), 1637–1646. https://doi.org/10.1007/s11548-016-1359-6
International Journal of Computer Assisted Radiology and Surgery
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