Brain MR image segmentation and bias field correction using adaptive fuzzy C means model

The imperfections in the radio-frequency coils or problems associated with the acquisition sequences may cause MRI intensity inhomogeneities, which may mislead image segmentation. Comparing the tradition fuzzy C means model, this paper adds the bias field information in the objective function for simultaneous correction of the bias field and accurately segmentation. In adaptive model, the bias field is modeled as a linear combination of a set of basis functions to ensure the smoothness and slowly varying, and can be easy estimated by computing the coefficients of this basis functions in every iteration. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.