Low order adaptive region growing for lung segmentation on plain chest radiographs

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.