Magnetic resonance image tissue classification using a partial volume model
Magnetic resonance image tissue classification using a partial volume model
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DOI：
10.1006/nimg.2000.0730
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发表时间：
2001-05-01
期刊：
影响因子：
5.7
通讯作者：
Leahy, RM
中科院分区：
文献类型：
--
作者：
Shattuck, DW;Sandor-Leahy, SR;Leahy, RM
我们描述了一系列低级操作，用于在 T1 加权磁共振图像 (MRI) 中分离和分类脑组织。我们的方法首先结合各向异性扩散过滤、边缘检测和数学形态学来去除非脑组织。我们通过将三次 B 样条增益场拟合到整个 MRI 体积中间隔的图像不均匀性的局部估计来补偿由于磁场不均匀性引起的图像不均匀性。通过将部分体积组织测量模型拟合到每个估计点附近的邻域直方图来计算局部估计。测量模型使用根据全局图像计算的平均组织强度和噪声方差值以及在直方图拟合期间为每个区域估计的乘法偏差参数。然后使用最大后验分类器将强度归一化图像中的体素分为六种组织类型。该分类器将部分体积组织测量模型与模拟大脑空间特性的吉布斯先验相结合。我们根据真实数据和虚拟数据验证算法的每个阶段。使用来自互联网大脑分割存储库的 20 个正常 MRI 大脑数据集的数据，与专家标记的数据相比，我们的方法实现了灰质 (GM) 的平均 kappa 指数为 kappa = 0.746 +/- 0.114，白质 (WM) 的平均 kappa 指数为 kappa = 0.798 +/- 0.089。与蒙特利尔神经学研究所 BrainWeb 模型中 12 卷的真实标签相比，我们的方法实现了 GM 的平均 kappa 指数 kappa = 0.893 +/- 0.041，WM 的 kappa = 0.928 +/- 0.039。 (C) 2001 年学术出版社。
We describe a sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for image nonuniformities due to magnetic field inhomogeneities by fitting a tricubic B-spline gain field to local estimates of the image nonuniformity spaced throughout the MRI volume. The local estimates are computed by fitting a partial volume tissue measurement model to histograms of neighborhoods about each estimate point. The measurement model uses mean tissue intensity and noise variance values computed from the global image and a multiplicative bias parameter that is estimated for each region during the histogram fit. Voxels in the intensity-normalized image are then classified into six tissue types using a maximum a posteriori classifier. This classifier combines the partial volume tissue measurement model with a Gibbs prior that models the spatial properties of the brain. We validate each stage of our algorithm on real and phantom data. Using data from the 20 normal MRI brain data sets of the Internet Brain Segmentation Repository, our method achieved average kappa indices of kappa = 0.746 +/- 0.114 for gray matter (GM) and kappa = 0.798 +/- 0.089 for white matter (WM) compared to expert labeled data. Our method achieved average kappa indices kappa = 0.893 +/- 0.041 for GM and kappa = 0.928 +/- 0.039 for WM compared to the ground truth labeling on 12 volumes from the Montreal Neurological Institute's BrainWeb phantom. (C) 2001 Academic Press.