Noise-Estimation-Based Fuzzy C-Means Clustering for Image Segmentation

Since a noisy image has inferior characteristics, its direct use in Fuzzy C-Means (FCM) often produces poor image segmentation results. Intuitively, using its ideal value (noise-free image) benefits FCM's robustness enhancement. Therefore, the realization of accurate noise estimation in FCM is a new and important task. In this chapter, we elaborate residual-driven Fuzzy C-Means (FCM) for image segmentation, which is the first approach that realizes accurate residual (noise/outliers) estimation and makes noise-free image participate in clustering. We propose a residual-driven FCM framework by integrating into FCM a residual-related regularization term derived from the distribution characteristic of different types of noises. Built on this framework, a weighted ℓ2-norm regularization term is presented by weighting mixed noise distribution, thus resulting in a universal residual-driven FCM algorithm in presence of mixed or unknown noises. In addition, we make a comparative study of residual-driven FCM and only existing noise-estimation-based FCM, i.e., deviation-sparse FCM. Finally, supporting experiments on synthetic, medical, and real-world images are conducted. The results demonstrate the superior effectiveness and efficiency of the proposed algorithm over its peers.