Fast Hybrid Level Set Model for Non-homogenous Image Segmentation Solving by Algebraic Multigrid
A novel hybrid fitting energy based active contours model in the level set framework is proposed. The method fuses the local image fitting term and the global image fitting term to drive the contour evolution, and a special extra term that penalizes the deviation of the level set function from a signed distance function is also included in our method, so the complex and costly re-initialization procedure is completely eliminated. Our model can efficiently segment the images with intensity inhomogeneity no matter where the initial curve is located in the image. In its numerical implementation, the Algebraic Multigrid (AMG) is used for breaking the restrictions on time step, compared with the traditional schemes, the AMG strategy can shorten the time consumption of the evolution process, this allows the level set to quickly reach the true target location. The extensive and promising experimental results on numerous synthetic and real images have shown that our method can efficiently improve the image segmentation performance, in terms of accuracy, efficiency, and robustness.