Abstract
This study investigates an active curve functional which measures a similarity between the distribution of an image feature on the curve and a model distribution learned a priori. The curve evolution equation resulting from the minimization of this contour-based functional can be viewed as a geodesic active contour with a variable stopping function. The variable stopping function depends on the distribution of image feature on the curve and, therefore, can deal with difficult cases where the desired boundary corresponds to very weak image transitions. We ran several experiments supported by quantitative performance evaluations over several examples of segmentation and tracking of the left ventricle inner and outer boundaries in cardiac magnetic resonance image sequences. The results are significantly more accurate than with region-based and edge-based functionals.