血管内超声图像初始轮廓提取算法的研究  

Research of Intravascular Ultrasound Image′s Initial Contour Extraction Algorithm

作　　者：王志东[1] 汪友生[1] 董路[1] 李冠宇[1] 

机构地区：[1]北京工业大学电子信息与控制工程学院,北京100124

出　　处：《计算机测量与控制》2014年第5期1490-1492,共3页Computer Measurement &Control

摘　　要：Snake模型在医学图像分割中的应用已经越来越广泛,但在应用该模型时,如何选取合适的初始轮廓是一个难题;在对血管内超声医学图像的研究基础上,提出了一种基于灰度信息与ROI区域的初始轮廓获取方法,根据IVUS图像的灰度特征对其进行自适应阈值分割以及面积滤波,然后获得分割轮廓点集进而得到snake初始轮廓点集;在matlab7.0环境分别对不同类别的2种IVUS图像的中外膜边缘提取进行仿真实验,实验证明该方法获得的初始snake轮廓较为逼近目标真实轮廓且适合于snake模型进行迭代收敛,由于其初始轮廓已较为接近目标真实轮廓,因此节省了snake模型的迭代次数,算法运行效率也优于手工提取初始轮廓的snake方法,可以较为方便的应用于实际领域。Snake model in medical image segmentation has become increasingly widespread, but in the application of the model, how to select the appropriate initial contour is a problem. In this paper, we propose a method to extract the ROI~ s initial contour based on the gray information of IVUS images . According to IVUS image＇ s grayscale features, firstly, adaptive thresholding and area filtering are used to reduce the complexity of the IVUS original images, then get splited contour points set and finally get the snake~ s initial contour points set. Two kinds of IVUS images ~ outer membrane extraction simulation experiments in Matlab7. 0 environment prove that the initial snake contour obtained by this method is suitable for snake model＇ s iteration, as its initial contour is close to the true outline of the target, it saves more time and improve the efficiency o~ the algorithm compared with manual extraction method, it can be more easily applied to practical fields.

关 键 词：血管内超声 自适应阈值分割 ROI区域 SNAKE模型 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]