Robust Image Segmentation Using Fuzzy C-Means Clustering with Spatial Information Based on Total Generalized Variation

Fuzzy c-means clustering (FCM) has proved highly successful in the manipulation and analysis of image information, such as image segmentation. However, the effectiveness of FCM-based technique is limited by its poor robustness to noise and edge-preserving during the segmentation process. To tackle these problems, a new objective function of FCM is developed in this work. The main innovation work and results of this paper are outlined as follows. First, a regularization operation performed by total generalized variation (TGV) is used to guarantee noise smoothing and detail preserving. Second, a weight factor incorporated into the spatial information term is designed to form nonuniform membership functions, which can contribute to the assignment of each pixel for the highest membership value. In addition, a regularization parameter is used to balance the respective importance of penalty between whole image and each neighborhood. The main advantage of this technique over conventional FCM-based methods is that it can reconstruct image patterns in heavy noise with only a small loss. We perform experiments on both synthetic and real images. Compared to state-of-the art FCM-based methods, the proposed algorithm exhibits a very good ability to noise and edge-preserving in image segmentation.