Cardiac segmentation with discriminant active contours

Dynamic tracking of heart moving is one relevant target in medical imaging and can be helpful for analyzing heart dynamics in the study of several cardiac diseases. For this aim, a previous segmentation problem of such structures is stated, based on certain relevant features (like edges or intensity levels, textures, etc.) Classical active models have been used, but they fail when overlapping structures or not well-defined contours are present. Automatic feature learning systems may be a powerful tool. Discriminant active contours present optimal results in this kind of problem. They are a kind of deformable models that converge to an optimal object segmentation that dynamically adapts to the object contour. The feature space is designed from a filter bank in order to guarantee the search and learning of the set of relevant features for optimal classification on each part of the object. Tracking of target evolution is obtained through the whole set of images, using information from the actual and previous stages. Feedback systems are implemented to guarantee the minimum well-separable classification set in each segmentation step. Our implementation has been proved with several series of Magnetic Resonance with improved results in segmentation in comparison to previous methods.