Left Ventricle Segmentation and Motion Analysis in MultiSlice Computerized Tomography

Abstract

Cardiac motion analysis is an important tool for evaluating the cardiac function. Accurate motion estimation techniques are necessary for providing a set of parameters useful for diagnosis and guiding therapeutical actions. In this chapter, the problem of cardiac motion estimation is presented. A short overview of techniques based in several imaging modalities is given where the machine learning techniques have played an important role. A feasible solution for left ventricle segmentation in multislice computerized tomography (MSCT) and for estimating the left ventricle motion is presented. This method is based on the application of support vector machines (SVM), region growing and a nonrigid bidimensional correspondence algorithm used for tracking the anatomical landmarks extracted from the segmented left ventricle (LV). Some experimental results are presented and at the end of the chapter a short summary is presented.

Similarity enhancement for automatic segmentation of cardiac structures in computed tomography volumes

“…Bravo et al [12], performed LV segmentation and motion analysis in MSCT sequences using support vector machines and a region growing technique. They proposed the validation test that considers several metrics obtaining an average position error of 1.38 mm.…”