Image segmentation via the continuous max-flow method based on chan-vese model

Abstract

The Chan-Vese model using variational level set method (VSLM) has been widely used in image segmentation, but its efficiency is a challenge problem due to high computation costs of curvature as well as the Eiknal equation constraint. In this paper, we propose a continuous Max-Flow (CMF) method based on discrete grap h cut approach to solve the VSLM for image segmentation. Firstly, we recast the original Chan-Vese model to a continuous max-flow problem via the primal-dual method and solve it using the alternating direction method of multipliers (ADMM). Then, we use the projection method to recover the continuous level set function for image segmentation expressed as a signed distance function. Finally, some numerical examples are presented to demonstrate the efficiency and accuracy of the proposed method.