- 《Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation》
内容：This paper proposes a new energy minimization method called multiplicative intrinsic component optimization (MICO) for joint bias field estimation and segmentation of magnetic resonance (MR) images. The proposed method takes full advantage of the decomposition of MR images into two multiplicative components, namely, the true image that characterizes a physical property of the tissues and the bias field that accounts for the intensity inhomogeneity, and their respective spatial properties. Bias field estimation and tissue segmentation are simultaneously achieved by an energy minimization process aimed to optimize the estimates of the two multiplicative components of an MR image. The bias field is iteratively optimized by using efficient matrix computations, which are verified to be numerically stable by matrix analysis. More importantly, the energy in our formulation is convex in each of its variables, which leads to the robustness of the proposed energy minimization algorithm.
推荐理由：该论文较详细讲述了处理MRI bias field estimation，同时附有代码供我们学习和理解其中的过程。
期刊：Magnetic Resonance Imaging****, vol. 32 (7), pp. 913-923, 2014
- 《MRI Tissue Classification and Bias Field Estimation Based on Coherent Local Intensity Clustering》
内容：主要讲MRI bias field estimation problem。
推荐理由：该文章引入了局部区域灰度来构建能量函数实现了分类和偏差场估计
期刊：IPMI 2009
3 《A Modified Fuzzy C-Means Algorithm for Bias Field Estimation and Segmentation of MRI Data》
内容： 利用了改进后的FCM解决了Bias Field Estimation and Segmention.
推荐理由：论文中对标准的模糊c-均值算法进行修改，来弥补灰度不均一的现象，同时引入pixel收到相邻pixel影响的正则化项，取得了较好的效果。
期刊：IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 21, NO. 3, MARCH 2002
4 《A Modified Fuzzy C-Means for Bias Field Estimation and Segmentation of Brain MR Image 》
内容：改进了FCM，用来解决Bias Field Estimation
推荐理由：该文章在第三篇文章的基础了添加了全局正则化项进行了改进，可以和第三篇论文结合对比起来看。
会议：2013 25th Chinese Control and Decision Conference (CCDC)
5 《A FUZZY C-MEANS BASED ALGORITHM FOR BIAS FIELD ESTIMATION AND SEGMENTATION OF MR IMAGES》
内容：该文章提出了一种新的算法，同时估计的偏置磁场和分割的组织磁共振图像。制定的算法在FCM目标函数修改算法包括一个偏置场被建模为一个线性一组基函数的组合。偏置场估计图像分割是同时实现的减少这种改进的模糊C均值的客观结果功能。对目标函数的迭代算法最小化我们提供的收敛到最优解在快的速度。我们的方法的突出优点是：其结果是独立的初始化，这使得强大的和完全自动化的和卓越的应用与其他方法相比，性能。所提出的方法已成功地应用于3-T MR图像并得到了理想的结果。
推荐理由：与3，4较相似，都是研究解决偏置场估计问题。