A fuzzy clustering algorithm via enhanced spatially constraint for brain MR image segmentation

Fuzzy clustering has been extensively 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 via enhanced spatially constraint for brain MR image segmentation. A novel spatial factor is proposed by incorporating the spatial information with a simple metric, which is fast and easy to implement. By taking the spatial direction into account based on the posterior and prior probabilities, 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 intensity inhomogeneity in the image. Experimental results demonstrate that the proposed algorithm can substantially improve the accuracy of brain MR image segmentation.