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

Document Type

Article

Department or Administrative Unit

Computer Science

Publication Date

3-19-2016

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[0,2π)" role="presentation">[0,2π)

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.

Comments

This article was originally published in International Journal of Computer Assisted Radiology and Surgery. The full-text article from the publisher can be found here.

Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.

Journal

International Journal of Computer Assisted Radiology and Surgery

Rights

Copyright © 2016, CARS

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