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程明,黄晓阳,黄绍辉,王博亮(厦门大学计算机科学系,厦门 361005)

摘 要
Directional region growing algorithm and its applications in vessel segmentation

Cheng Ming,Huang Shxiaoyang,Huang Shaohui,Wang Boliang()

Accurate extraction of the vasculature in medical images is prerequisite to structural analysis and further applications such as surgical planning. Region growing algorithm is a simple and effective method to extract thick blood vessels which makes use of the spatial continuity of the vascular tree, while the extraction result of small vessels like hepatic artery is unacceptable. In order to solve the problem that the continuity of tenuous vasculature is poor in medical images and vessel segmentation based on traditional region growing may lose distal branches, a directional region growing (DRG) algorithm is proposed which can skip the low gray area in the vasculature during the growing process. The algorithm grows towards the direction of the maximum gray around the grown region, and adds one voxel to the grown region in each iteration. The image is transformed into a tree after the growing process in which the seed point is the root. A trace back procedure beginning from the leaf nodes of the tree can finally determine the region of interest (ROI). The algorithm relaxes the conditions to determine ROI, and small area with low gray in the ROI is permitted. There are two time-consuming steps in the algorithm due to the enormous amount of data in 3D medical images, one is to determine the growing direction in each iteration, the other is to construct the paths from the seed point to leaf nodes during the trace back procedure. Data structure to improve the speed of the algorithm is discussed. The algorithm can be applied to images with any dimension. The algorithm is tested with 2D and 3D images. In both conditions, the segmentation results obtained by DRG contain more distal branches in comparison with traditional region growing algorithm. To some vein phase CT images with poor quality, the proposed algorithm can also generate better results. Four parameters should be appointed in the algorithm and the empirical values are given. The computational time of the algorithm on 3D images is several seconds, which is acceptable in clinical applications. The surface of the extracted vasculature is rough due to the discrete nature of digital images, and further study is needed to smooth the surface before visualization.