Automatic method for lung segmentation with juxta-pleural nodules from thoracic CT based on border separation and correction
Yanxia Sun, Jinke Wang · 2016
In this paper, a fully automatic method was proposed for lung segmentation with juxta-pleural nodules from CT. The approach consists of three phases: skin boundary detection, rough segmentation of lung contour, and pulmonary parenchyma refinement. Firstly, chest boundary is extracted through image aligning, morphology operation and connective region analysis. Secondly, diagonal-based border tracing is implemented for lung contour segmentation, with maximum cost path algorithm used for separating the left and right lungs. Finally, by arc-based border smoothing and concave-based border correction, the refined pulmonary parenchyma is obtained. The proposed scheme is evaluated on 20 volumes of chest scans, with volume difference (VD) 11.15 ± 69.63 cm3, volume overlap error (VOE) 3.5057 ± 1.3719%, average surface distance (ASD) 0.7917 ± 0.2741 mm, root mean square distance (RMSD) 1.6957 ± 0.6568 mm, maximum symmetric absolute surface distance (MSD) 21.3430 ± 8.1743 mm, and average time-cost 2 second per image. The preliminary results on accuracy and complexity prove that our scheme is a promising tool for lung segmentation with juxta-pleural nodules.