Deformable template and distribution mixture-based data modeling for the endocardial contour tracking in an echographic sequence

We present a new method to shape-based segmentation of deformable anatomical structures in medical images and validate this approach by detecting and tracking the endocardial border in an echographic image sequence. To this end, a global prior knowledge of the endocardial contour is captured by a prototype template with a set of admissible deformations to take into account its inherent natural variability over time. In this approach, the data likelihood model rely on an accurate statistical modeling of the grey level distribution of each class present in the image. The parameters of this distribution mixture are given by a preliminary estimation step which takes into account the distribution shape of each class. Then the tracking problem is stated in a Bayesian framework where it ends up as an optimization problem. This one is then efficiently solved by a genetic algorithm combined with a steepest ascent procedure. This technique has been successfully applied on synthetic images and on a real echocardiographic image sequence.