Automated model-based tissue classification of MR images of the brain
Automated model-based tissue classification of MR images of the brain
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DOI：
10.1109/42.811270
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发表时间：
1999-10-01
影响因子：
10.6
通讯作者：
Suetens, P
中科院分区：
文献类型：
--
作者：
Van Leemput, K;Maes, F;Suetens, P
我们描述了一种基于模型的脑磁共振（MR）图像的组织分类的全自动方法，该方法将分类与模型参数的估计交织在一起，从而在每次迭代时改进分类。该算法能够分割单谱和多谱MR图像，校正MR信号的不均匀性，并通过马尔可夫随机场（MRF）结合上下文信息。一个数字脑图谱包含有关组织类的空间位置的先验期望用于初始化算法。这使得该方法完全自动化，因此它提供了客观和可再现的分割，我们已经验证了模拟以及真实的MR图像的大脑的技术。
We describe a fully automated method for model-based tissue classification of magnetic resonance (MR) images of the brain, The method interleaves classification with estimation of the model parameters, improving the classification at each iteration. The algorithm is able to segment single- and multispectral MR images, corrects for MR signal inhomogeneities, and incorporates contextual information by means of Markov random Fields (MRF's). A digital brain atlas containing prior expectations about the spatial location of tissue classes is used to initialize the algorithm. This makes the method fully automated and therefore it provides objective and reproducible segmentations, We have validated the technique on simulated as well as on real MR images of the brain.