Texture classification and shape statistics variational approach for segmentation of left ventricle tagged MR images

Abstract

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.