Tohka Jussi, Zijdenbos Alex, Evans Alan
Digital Media Institute/Signal Processing, Tampere University of Technology, FIN-33101, Finland.
Neuroimage. 2004 Sep;23(1):84-97. doi: 10.1016/j.neuroimage.2004.05.007.
Due to the finite spatial resolution of imaging devices, a single voxel in a medical image may be composed of mixture of tissue types, an effect known as partial volume effect (PVE). Partial volume estimation, that is, the estimation of the amount of each tissue type within each voxel, has received considerable interest in recent years. Much of this work has been focused on the mixel model, a statistical model of PVE. We propose a novel trimmed minimum covariance determinant (TMCD) method for the estimation of the parameters of the mixel PVE model. In this method, each voxel is first labeled according to the most dominant tissue type. Voxels that are prone to PVE are removed from this labeled set, following which robust location estimators with high breakdown points are used to estimate the mean and the covariance of each tissue class. Comparisons between different methods for parameter estimation based on classified images as well as expectation--maximization-like (EM-like) procedure for simultaneous parameter and partial volume estimation are reported. The robust estimators based on a pruned classification as presented here are shown to perform well even if the initial classification is of poor quality. The results obtained are comparable to those obtained using the EM-like procedure, but require considerably less computation time. Segmentation results of real data based on partial volume estimation are also reported. In addition to considering the parameter estimation problem, we discuss differences between different approximations to the complete mixel model. In summary, the proposed TMCD method allows for the accurate, robust, and efficient estimation of partial volume model parameters, which is crucial to a variety of brain MRI data analysis procedures such as the accurate estimation of tissue volumes and the accurate delineation of the cortical surface.
由于成像设备的空间分辨率有限，医学图像中的单个体素可能由多种组织类型混合而成，这种效应称为部分容积效应（PVE）。部分容积估计，即估计每个体素内每种组织类型的含量，近年来受到了广泛关注。这项工作大多集中在混合体素模型上，这是一种部分容积效应的统计模型。我们提出了一种新颖的截尾最小协方差行列式（TMCD）方法来估计混合体素部分容积效应模型的参数。在该方法中，首先根据最主要的组织类型对每个体素进行标记。将容易出现部分容积效应的体素从这个标记集中去除，然后使用具有高崩溃点的稳健位置估计器来估计每个组织类别的均值和协方差。报告了基于分类图像的不同参数估计方法之间的比较，以及用于同时进行参数估计和部分容积估计的类似期望最大化（EM 类）过程。结果表明，即使初始分类质量较差，基于修剪分类的稳健估计器也表现良好。得到的结果与使用 EM 类过程得到的结果相当，但所需的计算时间要少得多。还报告了基于部分容积估计的真实数据的分割结果。除了考虑参数估计问题，我们还讨论了完整混合体素模型的不同近似之间的差异。总之，所提出的 TMCD 方法能够准确、稳健且高效地估计部分容积模型参数，这对于各种脑 MRI 数据分析过程至关重要，例如准确估计组织体积和精确描绘皮质表面。
