Robust fuzzy segmentation of magnetic resonance images

A new approach for the robust segmentation of magnetic resonance images is described. The approach is derived from a generalization of the objective function used in D.L. Pham and J.L. Prince's (1999) adaptive fuzzy c-means algorithm (AFCM). Within the objective function, an additional constraint is placed on the membership functions that forces them to be spatially smooth. Minimization of this objective function results in an unsupervised fuzzy segmentation algorithm that is robust to intensity inhomogeneity artifacts as well as noise and other artifacts. The efficacy of the algorithm is demonstrated on simulated magnetic resonance images.