A boundary extraction method based on Dual-T-Snakes and dynamic programming

The original proposal of active contour models, also called snakes, for image segmentation, suffers from a strong sensitivity to its initial position and can not deal with topological changes. The sensitivity to initialization can be addressed by dynamic programming (DP) techniques which have the advantage of guaranteeing the global minimum and of being more stable numerically than the variational approaches. Their disadvantages are the storage requirements and computational complexity. In this paper we address these limitations of DP by reducing the region of interest (search space) through the use of the Dual-T-Snake approach. The solution of this method consists of two curves enclosing each object boundary which allows the definition of a more efficient search space for a DP technique. The resulting method (Dual-T-Snake plus DP) inherits the capability of changing the topology and avoiding local minima from the Dual-T-Snake and the global optimal properties of the dynamic programming. It can be also extended for 3D