Local curvature constrained Level Set segmentation using a Spectral Bi-Partitioning algorithm

Coupling the level set method and graph cut optimization method in a complementary fashion demonstrates a superior performance in two-class segmentation problems. The basic idea is to first define an energy function according to curve evolution using Level Set method and then construct a similarity graph with well selected edge weights based on the boundary curvature values, which is further optimized via our proposed spectral bi-partitioning algorithm. By the way, our model shares advantages of both level set methods and graph cut algorithms. Difficulties are found in computing due to the fact that curvature values are very critic and not easy to manipulate, start working with those values leads to instability in computing. To avoid this problem, we have chosen images with two-class segmentation problems. Accordingly, the proposed method uses local criteria and yet produces results that reflect global properties of the image.