GLOBAL MINIMUM FOR ACTIVE CONTOUR MODELS - A MINIMAL PATH APPROACH

A new boundary detection approach for shape modeling is presented. It detects the global minimum of an active contour model's energy between two end points. Initialization is made easier and the curve is not tr apped at a local minimum by spurious edges. We modify the ''snake'' en ergy by including the internal regularization term in the external pot ential term. Our method is based on finding a path of minimal length i n a Riemannian metric. We then make use of a new efficient numerical m ethod to find this shortest path. It is shown that the proposed energy , though based only on a potential integrated along the curve, imposes a regularization effect like snakes. We explore the relation between the maximum curvature along the resulting contour and the potential ge nerated from the image. The method is capable to close contours, given only one point on the objects' boundary by using a topology-based sad dle search routine. We show examples of our method applied to real aer ial and medical images.