Local-based fuzzy clustering for segmentation of MR brain images

Accurate segmentation of magnetic resonance images (MRI) corrupted by intensity inhomogeneity is a challenging problem and has received an enormous amount of attention lately. On the basis of the local image model, we propose a different segmentation method for MR brain images without estimation and correction for intensity heterogeneity. Firstly, we obtain clustering context based on the distributing disciplinarian in anatomy that gray matter (GM) is always between white matter (WM) and cerebrospinal fluid (CSF) in brain, which ensure the three tissues exist together in each one. Then the size of the context is optimized by a minimum entropy criterion. Finally, FCM algorithm is independently performed in each context to calculate the degree of membership of a pixel to each tissue class. The proposed methodology has been evaluated for simulated images and shown the better results.