The Vascular Calcification Image Segmentation Based on CV Model

In order to accurately segment the calcification of the vascular in medical images, a level set segmentation method based on CV model is designed. Firstly, the image preprocessing is carried out, which including that the median filter is used to remove the image noise, and the histogram equalization is used to enhance the image contrast. Next, according to the actual need we segment the region of interest including the vascular and calcification? And then, the level set segmentation method based on the CV model is adopted to segment the calcified points which adhere to the wall of the vascular. Finally, the morphological operation is used to eliminate the small noise and hole. A large number of clinical vascular calcification images were tested, and the experimental results clearly indicated that the level set segmentation method based on CV model can effectively segment the calcification, and detected the location, size, shape, etc... At the same time, the contrast experiment was designed, and the CV model was compared with OTSU threshold segmentation and hill climbing method. The results showed that the CV model is more accurate, the edge is smoother and clearer. The CV model segmentation algorithm is accurate and effective for detecting vascular calcification, and is convenient for further measurement of the calcification, that can provide effective evidence for clinical diagnosis.