Robust topology-adaptive snakes for image segmentation

Snakes'-based segmentation techniques have a variety of applications in computer vision. Traditional snakes however are well known to be topologically inflexible. They are incapable of dealing with more complicated object shapes as well as multiple-object scenes since the snakes require that the topology of object structures of interest must be known in advance. This paper introduces a robust topology-adaptive snake, based on the attractable snake model [6], to extend the snakes' topological adaptability. Three embedded schemes: the robust self-looping process, the efficient contour-merging and the improved adaptive interpolation scheme, are involved. Experiment results show that the new snake model is able to; consistently evolve towards its target objects, handle topological changes (i.e. splitting or merging) automatically as necessary and conform to more complicated geometries and topologies, without restrictive requirements on the initial conditions of the snake or on its deformation movement. © 2002 Elsevier Science B.V. All rights reserved.