Multilayer Level Set Method for Multiregion Image Segmentation

The purpose of this study is to propose a novel method of a partition of an image domain into an adaptive number of regions using a multilayer foreground-filled method. First, two coupled curves based on a three-region Chan-Vese model, which is built based on the techniques of evolving simple closed planar curves by an explicit correspondence to minimize energy functional containing a fitting term and a regularization term, evolve simultaneously to segment images containing two objects and one background region in each image layer. Second, a foreground-filled technique is used to generate a new image and the three-region Chan-Vese model is repeated to segment the new image for the next image layer. To avoid the long iteration process for level set evolution, an efficient termination criterion is presented on the basis on the length change of an evolving curve. This iterative process is repeated until the background image layer is detected. Numerical experiments on some synthetic and real images have demonstrated the efficiency and robustness of our method.