基于活动轮廓模型和统计特征的血管内超声图像的边缘提取

Edge Detection of IVUS Image Based on Active Contour Model and Statistical Features

血管内超声(IVUS)图像边缘的提取对冠状动脉疾病的诊断和治疗有着重要的意义。为此,提出了一种用于自动提取血管内超声图像内、外膜边缘的方法。这种方法基于活动轮廓模型和超声图像的对比度特征量以及Rayle igh分布统计特性,有效利用动态规划和启发式图搜索方法,分别在不同的代价函数形式下,对血管内超声图像内、外膜边缘进行自动提取。实验结果表明,和以往的提取方法相比,该方法算法简单,准确性较高,对序列图像处理的可重复性和鲁棒性较强,是一种较好的全局最优化算法。

The edge detection of intravascular ultrasound （IVUS） image has great significance in the diagnosis and treatment of the coronary artery disease. In this paper, a new method used for automatically detecting the intima and adventitia edge of IVUS image is presented. The method bases on active contour model, the contrast and Rayleigh distribution characteristics of IVUS image, effectively uses dynamic programming and heuristic graph searching , and automatically detects the intima and adventitia edge of IVUS image with the different cost functions. Experiments show that,compared with the former method of edge detection, our method is algorithmically simple, statistically accurate,reproducible and robust in sequential IVUS frames, and the algorithm is a kind of global optimal one.