Ground-glass nodule segmentation in chest CT images using asymmetric multi-phase deformable model and pulmonary vessel removal

A novel ground-glass nodule (GGN) segmentation method that separates solid components and ground-glass opacity (GGO) in chest CT images has been developed. This technique first extracts the initial solid component and GGO using intensity-based segmentation with histogram modeling. The extracted regions are then refined using an asymmetric multi-phase deformable model with an intensity-constrained averaging function. Finally, vessel-like structures are removed based on multi-scale shape analysis. Experimental evaluations using datasets from Seoul National University Hospital (SNUH) and Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) demonstrated high segmentation accuracy for the proposed method, showing significant correlation with manual segmentation. This innovative approach enhances GGN segmentation accuracy, proving the effectiveness of the asymmetric multiphase deformable model and pulmonary vessel removal in chest CT image analysis. The proposed GGN segmentation algorithm have been transferred to AVIEW Lung Screen, Coreline Soft.