Fast single parameter level set segmentation based on semi-implicit schemes
Zhou Ze-kui · Journal of Zhejiang University(Engineering Science) · 2010
To the lack of conventional level set methods for image segmentation,the too many parameters in the model and the lower computationally implementation,this work proposed a novel level set method for faster segmentation effectively.The method improved the Chan-Vese model by adding apenalized energy term,replacing the dirac function with the norm of level set function gradient and reserving only the parameter of the length term.The new PDE model needs no re-initialization and gives better globe optimization by less evolution loops.Besides,a new semi-implicit scheme was selected for shortening the time of every loop.In order to search the rules between the time step and the single parameter,an evolutional criterion for ending segmentation were introduced during the iterative process.The experimentations for synthesized,biomedical images and video sequences show that the new approach needs fewer iterative steps,the algorithm is faster and more accurate than the traditional level set methods,and it can satisfy the stability and real time requirement in the video tracking.