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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.

An automatic approach based on the generalized Hough transform (GHT) and unsupervised clustering technique to obtain the endocardial surface is proposed. The approach is applied to multi slice computerized tomography (MSCT) images of the heart. The first step is the initialization, where a GHT-based segmentation algorithm is used to detect the edocardial contour in one MSCT slice. The centroid of this contour is used as a seed point for initializing a clustering algorithm. A two stage segmentation algorithm is used for segmenting the three-dimensional MSCT database. First, the complete database is filtered using mathematical morphology operators in order to improve the left ventricle cavity information in these images. The second stage is based on a region growing method. A seed point located inside the cardiac cavity is used as input for the clustering algorithm. This seed point is propagated along the image sequence to obtain the left ventricle surfaces for all instants of the cardiac cycle. The method is validated by comparing the estimated surfaces with respect to left ventricle shapes drawn by a cardiologist. The average error obtained was 1.52 mm.

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