Li Chunming, Gore John C, Davatzikos Christos
Center of Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia 19104, USA.
Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA.
Magn Reson Imaging. 2014 Sep;32(7):913-23. doi: 10.1016/j.mri.2014.03.010. Epub 2014 Apr 30.
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. The MICO formulation can be naturally extended to 3D/4D tissue segmentation with spatial/sptatiotemporal regularization. Quantitative evaluations and comparisons with some popular softwares have demonstrated superior performance of MICO in terms of robustness and accuracy.
本文提出了一种名为乘法内在成分优化（MICO）的新能源最小化方法，用于磁共振（MR）图像的联合偏置场估计和分割。该方法充分利用了将MR图像分解为两个乘法成分，即表征组织物理特性的真实图像和解释强度不均匀性的偏置场，以及它们各自的空间特性。通过旨在优化MR图像两个乘法成分估计的能量最小化过程，同时实现偏置场估计和组织分割。通过使用高效的矩阵计算对偏置场进行迭代优化，经矩阵分析验证其在数值上是稳定的。更重要的是，我们公式中的能量在其每个变量中都是凸的，这导致了所提出的能量最小化算法的鲁棒性。MICO公式可以自然地扩展到具有空间/时空正则化的3D/4D组织分割。与一些流行软件的定量评估和比较表明，MICO在鲁棒性和准确性方面具有卓越的性能。
