带标记线核磁共振(MR)图像能够提供了大量的运动信息，为实现左心室的运动重建提供了有利条件，但图像中存在灰度的不一致性、弱边界、伪影、标记线的影响等现象，这些都给带标记线左心室MR图像的分割带来了困难。目前带标记线核磁共振图像的左心室分割主要靠人工完成，为此提出了一种自动分割方法，它是基于分级处理的分割方法，主要由3部分组成：首先用数学形态学的方法实现左心室的自动定位；然后用K均值聚类、模板匹配和基于骨架的心肌形状恢复方法给出左心室的内外初始轮廓线；最后用改进的水平集方法对初始轮廓线进行演化而得到最终结果。实验结果证明，此方法有较强的鲁棒性，是行之有效的方法。

Tagged MR images provide much motion information, which provides advantage for motion reconstruction of left ventricle(LV), but region inhomogeneity, weak edges, artifacts and influence of tag lines can emerge in images, so the segmentation of tagged left ventricle MR images is difficult. At present, it is almost accomplished manually. This paper introduced an automatic segmentation method of tagged left ventricle MR images, which is based on multistage hybrid processing and is composed of three parts. First the left ventricle is located using morphological method, then whose inner and outer contours are initialized using k-mean clustering, templet matching and the myocardium shape restoration based on skeleton. At last, the initial contour lines are evolved using improved level set method to achieve object boundaries. The performance of this new approach is demonstrated using tagged MR images, and experimental results proved the robustness and effectiveness of this method.