Vascular segmentation algorithm using locally adaptive region growing based on centerline estimation

In this paper, we propose a new region-based approach on the basis of centerline estimation, to segment vascular networks in 3D CTA/MRA images. The proposed algorithm is applied repeatedly to newly updated local cubes. It consists of three tasks; local region growing, surfacic connected component labeling, and next local cube detection. The cube size is adaptively determined according to the estimated diameter. After region growing inside a local cube, we perform the connected component labeling procedure on all 6 faces of the current local cube (surfacic component labeling). Then the detected surfacic components are put into a queue to serve as seeds of following local cubes. Contrary to conventional centerline-tracking methods, the proposed algorithm can detect all bifurcations without any restriction because a region-based method is used at every local cube. And by confining region growing to a local cube, it can be more effective in producing prospective results. It should be noticed that the segmentation result is divided into several branches, so a user can easily edit the result branch-by-branch. The proposed method can automatically generate a flyway in a virtual angioscopic system since it provides a tree structure of the detected branches.