Biomedical active segmentation guided by edge saliency

Deformable models are very popular approaches in biomedical image segmentation. Classical snake models are edge-oriented and work well if the target objects have distinct gradient values. This is not always true in biomedical imagery, which makes the model very dependent on initial conditions. In this work we propose an edge-based potential aimed at the elimination of local minima due to undesired edges. The new approach integrates knowledge about the features of the desired boundaries apart from gradient strength and uses a new method to eliminate local minima, which makes the segmentation less sensitive to initial contours.