Segmentation of pulmonary vascular tree by incorporating vessel enhancement filter and variational region-growing.

BACKGROUND: Automatic segmentation of pulmonary vascular tree in the thoracic computed tomography (CT) image is a promising but challenging task with great clinical potential values. It is difficult to segment the whole vascular tree in reasonable time and acceptable accuracy. OBJECTIVE: To develop a novel pulmonary vessel segmentation approach by incorporating vessel enhancement filters and the anisotropic diffusion filter with the variational region growing. METHODS: First, the airway wall from the lung lobes is eliminated from CT images by using multi-scale morphological operations. Second, a Hessian-based multi-scale vesselness filter and medialness filter are applied to detect and enhance the potential vessel. Third, an anisotropic diffusion filter is used to remove noise and enhance the tube-like structures in CT images. Last, the vascular tree is segmented by applying variational region growing algorithm. RESULTS: Applying to the CT images collected from the entire dataset of VESSEL12 challenge, we achieved an average sensitivity of 92.9%, specificity of 91.6% and the area under the ROC curve of AUC = 0.972. CONCLUSIONS: This study demonstrated feasibility of segmenting the pulmonary vessel effectively by incorporating vessel enhancement filters and the anisotropic diffusion filter with the variational region growing algorithm. Our method cannot only segment both large and peripheral vessels, but also distinguish the vessels from the adjacent tissues, especially the airway walls.