期刊
NEUROCOMPUTING
卷 275, 期 -, 页码 1002-1011出版社
ELSEVIER
DOI: 10.1016/j.neucom.2017.09.053
关键词
Lung segmentation; Chest radiographs; Contrast enhancement; Region growing; Adaptive graph cut
This study proposes a computer-aided region segmentation for the plain chest radiographs. It incorporates an avant-garde contrast enhancement that increases the opacity of the lung regions. The region of interest (ROI) is localized preliminarily by implementing a brisk block-based binarization and morphological operations. Further improvement for region boundaries is performed using a statistical-based region growing with an adaptive graph-cut technique that increases accuracy within any dubious gradient. Assessed on a representative dataset, the proposed method achieves an average segmentation accuracy of 96.3% with low complexity on 256p resolutions. (c) 2017 Elsevier B.V. All rights reserved.
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