A hybrid level set approach for efficient and reliable image segmentation

This article is concerned with a level set segmentation algorithm which hybridizes gradient-based methods and the Mumford-Shah (gradient-free) method, for an efficient and reliable segmentation. We introduce a new strategy for the complementary functions u^plusmn , which is computed such that the difference between their average and the given image are able to introduce a reliable driving force for the evolution of the level set function. An effective method of background subtraction is suggested in order to improve reliability of the new model. An incomplete (linearized) alternating direction implicit (ADI) method is applied for an efficient time-stepping procedure. For a fast convergence, we also suggest effective initialization strategies for the level set function. The resulting algorithm has proved to locate the desired edges satisfactorily in 2-4 ADI iterations