Multiphase CV model integrated with improved FCM algorithm
结合改进FCM算法的多相位CV模型

Chan-Vese model, which has better ability to handle the blurry boundary and complex topological structures in images, has been widely used in image segmentations. However, the effect on segmentation in the images with intensity inhomogeneity and multiple-objects is less satisfying. Fuzzy c-means clustering(FCM) algorithm works as an unsupervised classification method has been applied in object identification and image segmentation. Nevertheless, it is sensitive to noise because of taking no account on the spatial information. Arming at these problems, a multiphase CV model integrated with improved FCM algorithm is proposed. First, the classes of the intensity are calculated based on the histogram statistics, and the spatial information computed in the neighborhood revise the grade of membership. The improved FCM algorithm applied with the region fitting term of CV model, working as the reliance of evolving the level-set curve. Anisotropic local template is then used to handle the different objects so as to control the split-up of the contour accurately and segment more objects in less time.