Fuzzy c-means clustering with a new regularization term for image segmentation

We present a new fuzzy c-means algorithm for image segmentation by introducing a novel spatially constrained Student´s t-distribution and a new regularization term. Firstly, considering that conventional distribution models lack spatial information and the multivariate Student´s t-distribution is heavily tailed, we propose a new way to incorporate spatial information between neighboring pixels into the Student´s t-distribution based on Markov random field (MRF) in order to enhance robustness. Secondly, the new regularization term, inspired by the geodesic active contour (GAC) with a strong ability in capturing boundary, can preserve the details of edges and further enhance its robustness to noise and outliers by capitalizing on the local context information and edge information. Finally, in comparison to other Markov random fields that are complex and computationally expensive, the parameters are easily optimized with the EM algorithm in our proposed method. The proposed algorithm demonstrates the robustness and effectiveness, compared with other state-of-the-art methods on synthetic and real images.