基于图划分的形状统计主动轮廓模型心脏MR图像分割

Graph Cuts and Shape Statistics Based Cardiac MR Image Segmentation Using Active Contours Model

为有效分析心脏功能,高精度分割左、右心室是必要的.心脏MR图像中存在图像灰度不均,左、右心室及周围其它组织灰度接近,存在弱边缘、边缘断裂及噪声造成边缘模糊等现象,给精确分割左、右心室轮廓带来困难.本文在基于图划分的主动轮廓方法基础上,通过对训练形状进行配准及变化模式分析,定义左、右心室轮廓形状变化允许空间,提出基于图划分的形状统计主动轮廓模型来分割心脏MR图像.该方法通过图划分理论将图像分割问题转化为最优化问题,所以能够得到全局最优解,具有较大的捕捉范围.还引入形状统计来引导曲线的演化,有效处理曲线演化时存在的边缘泄漏问题,提高分割精度.实验结果表明,本文方法较以往方法具有更高的分割精度和更好的稳定性,为临床应用提供一种较可行的方法.

To analyze heart function effectively, it is necessary to segment the left and right ventricles precisely. In cardiac MR images, the weak edges, broken boundaries, region inhomogeneity and noises cause difficulties in segmenting the contours of left and right ventricle precisely. In this paper, the training samples are aligned and analyzed, and the allowable shape space of the left and right ventricles is constructed. An active contours model based on graph cuts and shape statistics is p segmentation of cardiac MR images. It uses graph cuts based active contours （GCBAC） to convert the image segmentation into the globally optimal partition after transforming the image into a graph. Next, GCBAC uses graph cuts to iteratively deform the contour. Consequently, it has a large capture range. Then, the shape statistics is introduced into GCBAC. The introduction of shape statistics prevents the deformation curve form leaking out of actual boundaries. Experimental results demonstrate the proposed method achieves a higher segmentation precision and a better stability than other approaches and it provides a feasible way for clinical applications.