基于区域增长与局部自适应C-V模型的脑血管分割

提出了一种针对TOF MRA(time-of-flight magnetic resonance angiography)磁共振图像的双重分割脑血管提取方法。首先结合高斯滤波，采用二维OTSU算法，结合MIP(maximum intensity projection)图像获得三维血管种子点，定义全局与局部信息相结合的区域增长规则，通过区域增长算法对血管进行粗分割；然后，采用 Catt 扩散模型对体数据场进行各向异性滤波，提出了局部自适应C-V模型，将初步分割结果作为自适应活动轮廓模型的初始轮廓线进行二次分割。实验结果表明，该算法不仅能够有效分割脑血管粗大分支，而且还能精确提取脑血管的细小结构。

Cerebrovascular Segmentation Based on Region Growing and Local Adaptive C-V Model

This paper presents an effective approach to extract cerebrovascular tree from time-of-flight (TOF) magnetic resonance angiography (MRA) images. The approach consists of two segmentation stages. In the first stage, Gaussian filtering is implemented for the 3D volumetric field. By virtue of the maximum intensity projection (MIP) image segmented by the two dimensional OTSU algorithm, 3D vessel seeds are obtained. The region growing rule is defined by combining the global information with the local information, and then the rough segmentation is implemented by the region growing algorithm. In second stage, the original volume data is filtered by an anisotropic filtering based on Catt diffusion. A local adaptive C-V model is proposed, and the initial contour of the model is set by employing the first segmented vessels. Then the accurate segmentation is realized by the contour evolution. Experimental results show that the proposed algorithm is not only able to effectively segment the thick vessel, but also able to accurately extract the thinner vessels with weak boundaries.