Title:  Hybrid level set algorithms for efficient and reliable segmentation
Author: Seongjai Kim

Abstract:
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 $u^{\pm}$,
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 in 2-4
ADI iterations.