结合纹理与形状的Tagged MR图像左心室分割算法

Texture Classification and Shape Statistics Variational Approach for Segmentation of Left Ventricle Tagged MR Images

分割带标记线核磁共振(tagged MR)图像是左心室运动重建的前提.由于标记线的加载破坏了左心室的轮廓边缘和区域灰度一致性,再加上乳突肌的存在,使带标记线核磁共振图像的左心室内外轮廓分割变得相当困难.在变分框架下,将纹理分类信息与形状统计先验知识引入Mumford-Shah模型中,提出了一种改进的分割带标记线核磁共振图像的左心室内外轮廓的方法.该方法基于支持向量机对S滤波器组提取的纹理特征的分类结果,构造了一种新的图像能量表示;针对乳突肌及边缘断裂现象,引入形状统计先验信息来约束曲线的演化.因为分割过程利用了有监督学习策略,较好地克服了标记线对左心室区域灰度的影响,提高了分割精度.实验结果表明,该方法较以往方法具有更高的分割精度和更好的稳定性.

Segmentation of left ventricle tagged MR images is the basis of ventricular motion reconstruction. In left ventricle tagged MR images, the boundaries are often obscured or corrupted by the tag lines and region inhomogeneity as well as the existence of papillary muscles. These factors increase the difficulty of segmenting the inner and outer contour of left ventricle precisely. This paper introduces texture classification information and shape statistical knowledge into the Mumford-Shah model and presents an improved texture classification and shape statistics variational approach for the segmentation of inner and outer contour of left ventricle. It uses the output of support vector machine （SVM） classifier relying on S filter banks to construct a new region-based image energy term. This approach can overcome the influence of tag lines because it makes use of the supervised classification strategy. The introduction of shape statistics can improve the segmentation with broken boundaries. Segmentation results of an entire cardiac period on an identical image layer and a comparison of mean absolute distance analysis between contours generated by this approach and that generated by hand demonstrate that this method can achieve a higher segmentation precision and a better stability than other various approaches.