In this paper, we propose an improved method for simultaneous estimation of the bias field and segmentation of tissues
for magnetic resonance images, which is an extension of the method in. Firstly, the bias field is modeled as a linear
combination of a set of basis functions, and thereby parameterized by the coefficients of the basis functions. Then we
model the distribution of intensity in each tissue as a Gaussian distribution, and use the maximum a posteriori probability
and total variation (TV) regularization to define our objective energy function. At last, an efficient iterative algorithm
based on split Bregman method is used to minimize our energy function at a fast rate. Comparisons with other
approaches demonstrate the superior performance of this algorithm.