14 February 2012 A parametric statistic model and fast algorithm for brain MR image segmentation and bias correction
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Abstract
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.
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Tianming Zhan, Zhihui Wei, Liang Xiao, Ling Qian, "A parametric statistic model and fast algorithm for brain MR image segmentation and bias correction", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83142I (14 February 2012); doi: 10.1117/12.910782; https://doi.org/10.1117/12.910782
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