Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6864973 | Neurocomputing | 2018 | 10 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Peter Chondro, Chih-Yuan Yao, Shanq-Jang Ruan, Li-Chien Chien,