Improved image segmentation method based on fast level set and C-V model

Aim at solving the problem that the high computational complexity of level set methods excludes themselves from many real-time applications, an improved image segmentation method based on the fast level set algorithm is proposed in this paper. The proposed algorithm adopts an improved fast level set with a single list to realize the curve evolution, and it uses the binary fitting terms of the C-V model to design the speed function of curve evolution, preserving the global optimization characteristic of the C-V model. In addition, a termination criterion based on the number changing of contour points in the single list is proposed to ensure that the evolving curve can automatically stop on the true boundaries of objects. Experimental results show that the proposed algorithm can significantly improve the segmentation speed and can efficiently segment the noisy images.