结合改进FCM算法的多相位CV模型

Chan-Vese模型以其能较好地处理图像的模糊边界和复杂拓扑结构而广泛运用于图像分割中。但对于灰度不均匀性和多目标的分割效果较差。模糊聚类（FCM）算法作为一种无监督聚类算法已成功应用到目标识别和图像分割等领域。然而FCM算法没有考虑像素的空间信息对噪声敏感。针对这些问题，提出一种结合改进FCM算法的多相位CV模型。首先，基于直方图统计灰度种类、并利用邻域内计算的空间信息修正隶属度函数，这样克服了灰度不均匀性和噪声的影响。再将改进后的FCM算法应用到CV模型的区域检测项，可较准确地使像素点归类，以此作为曲线的演化依据。在演化时采用一种各项异性的模板来控制轮廓线的及时分裂，在较短时间内分割出更多目标。

Multiphase CV model integrated with improved FCM algorithm

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.