In order to accurately segment the calcification of the vascular in medical images, a level set segmentation method based on C-V model is designed. Firstly,the image preprocessing is carried out,which including removing the image noise and enhancing the image contrast. Next, the interested regions including the vascular and calcification are segmented. And then, the level set segmentation method based on the C-V model is adopt to segment the calcified points which adhere to the wall of the vascular. Finally,the morphological operation is used to eliminate the noise and hole. A large number of clinical vascular calcification images were tested, and the experimental results clearly indicated that the method in this paper can effectively segments the calcification,and detected the location,size,shape,etc.. At the same time, the C-V model was compared with OTSU threshold segmentation and hill climbing method., and the contrast results showed that the C-V model is more accurate,the edge is smoother and more clear,and is convenient for further measurement and diagnosis of the calcification.%为了准确分割出医学图像中血管的钙化点,设计并实现了一种基于C-V模型的水平集图像分割方法.首先进行去噪和对比度增强预处理,接下来分割出图像中感兴趣的血管和钙化点区域,然后利用C-V模型水平集分割方法分割血管壁上的钙化点目标,最后采用形态学方法消除分割结果中孤立的噪声和孔洞.针对大量的临床血管钙化图像进行了算法的测试,实验结果表明:能有效分割出血管中的钙化灶,准确检测出血管中钙化的位置、大小、形态等.将C-V模型分割方法与OTSU阈值分割、登山法分割方法进行比较,结果表明C-V模型分割方法对于钙化点的分割更准确,边缘更平滑,更清晰,方便对钙化点进行进一步的测量和诊断.
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