Fast Image Segmentation Based on Single-parametric Level-Set Approach

An improved level set framework for fast segmentation based on single parameter is presented. The traditional level set methods for image segmentation need inevitably too many parameters adjustment and they have usually lower computationally implementation. To solve this problem, the proposed method improves the C-V PDE model by adding a penalized energy term and replacing the dirac function with the norm of level set function gradient. Besides, only the parameter of the length term is reserved in the model and an evolution criterion is introduced for the value rules of this single parameter. The experimental results of synthesized and biomedical images show that the new method is faster and more robust. Moreover, the new method has more extensive adaptability on account of the zero level set function being set anyplace freely and the single parameter adjustment convenience.