A fuzzy clustering algorithm with robust spatially constraint for brain MR image segmentation

Fuzzy clustering algorithms have been widely used in brain magnetic resonance (MR) image segmentation. However, due to the existence of noise and intensity inhomogeneity, many segmentation algorithms suffer from limited accuracy. In this paper, we propose a fuzzy clustering algorithm with robust spatially constraint for accurate and robust brain MR image segmentation. A novel spatial factor is proposed by incorporating the spatial information amongst neighborhood pixels with a simple metric. A new weight factor, which utilizes the intensity information of the original image, is constructed to filter the posterior and prior probabilities in the spatial neighborhood. The proposed method can preserve more details and overcome the over-smoothing disadvantage. Finally, the fuzzy objective function is integrated with the bias field estimation model to overcome the intensity inhomogeneity in the image and segment the brain MR images. Experimental results demonstrate that the proposed algorithm can substantially improve the accuracy of brain MR image segmentation.