结合统计模型和曲线演化的左心室MRI图像分割

Left Ventricle MRI Image Segmentation by Unifying Statistic Model and Curves Evolving

提出结合区域统计模型和图像梯度信息的MRI图像分割算法。由于心脏的变形和血液的流动,MRI图像中出现弱边界、局部梯度极大值区域、伪影等现象。基于图像梯度构造停止项的水平集方法难以分割此类图像。本文提出两阶段图像分割算法。首先结合先验知识和直方图,确定图像中像素的类别总数。用极大似然估计原理求出每一类的先验概率和概率分布参数,根据像素属于感兴趣区域(ROI)的后验概率构造水平集速度函数,通过曲线演化获取ROI的粗边界。然后再使用图像梯度构造速度函数对边界进行细化。实验结果表明,本文算法能够有效分割心脏MRI图像。

An MRI image segmentation algorithm is proposed by unifying region statistic model and image gradient information. Due to cardiac deformation and blood flowing, weak edges, local gradient maximum regions and artifacts often can be found in the MRI images. The level set method which constructs stopping term with image gradient intensity cannot segment those cardiac MRI images accurately. A two-stage algorithm is thus proposed to address the difficulties. Firstly, incorporating prior knowledge about the cardiac MRI and the image histogram, the populations of pixels are given . The prior probabilities of those classes and the parameters of the Gaussian distributions are estimated with Maximum-likelihood principle. With the posterior probability of pixel belonging to ROI, the velocity function of level set is constructed to search for the rough boundary of ROI. Next, another velocity function based on the gradient vector flow is designed to locate the edges accurately . The experimental results demonstrate the effectiveness of the segmentation algorithm.