Myocardial border detection from ventriculograms using support vector machines and real-coded genetic algorithms

In this research a two step method for left ventricle segmentation based on landmark detection and evolutionary snakes is reported. The proposed approach is applied to human heart angiograms. Several anatomical landmarks located on the left ventricle are obtained using support vector machines. The training stage is performed based on a set of windows of size 31×3131×31 including landmarks and non-landmarks pixel patterns. The support vector machines use a radial basis function kernel and the structural risk minimization principle as the inference rule. During the training stage, no false positives are obtained and during the detection stage a 97.94% of recognition is attained. The estimated landmark location is used for constructing an approximate myocardial border. This contour is a deformable model that is optimized using a real-coded genetic algorithm. A validation is performed by comparing the estimated contours with respect to contours manually traced by two cardiologists. From this validation stage the maximum of the average contour error considering 6 angiographic sequences (a total of 178 images) is 4.93%.