Segmentation of brain tissue from magnetic resonance images.
Segmentation of brain tissue from magnetic resonance images.
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DOI：
10.1016/s1361-8415(96)80008-9
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发表时间：
1996-06-01
影响因子：
10.9
通讯作者：
Kikinis, R
中科院分区：
文献类型：
--
作者：
Kapur, T;Grimson, W E;Kikinis, R
由于图像的复杂性，以及缺乏完全捕获每个结构中可能的变形的解剖模型，医学图像的分割是一个具有挑战性的问题。大脑是一个特别复杂的结构，它的分割是许多问题的重要步骤，包括形态学的时间变化检测和手术规划的三维可视化研究。我们提出了一种从磁共振图像中分割脑组织的方法，该方法结合了计算机视觉文献中的三种现有技术：期望/最大化分割，二进制数学形态学和活动轮廓模型。这些技术中的每一种都是针对脑组织分割问题定制的，因此所得方法比其组件更鲁棒。最后，我们提出了这种方法的IBM的超级计算机电力可视化系统的数据库中的20个大脑扫描，每个256 × 256 × 124体素的并行实施的结果，并验证这些结果对神经解剖学专家产生的分割。
Segmentation of medical imagery is a challenging problem due to the complexity of the images, as well as to the absence of models of the anatomy that fully capture the possible deformations in each structure. The brain is a particularly complex structure, and its segmentation is an important step for many problems, including studies in temporal change detection of morphology, and 3-D visualizations for surgical planning. We present a method for segmentation of brain tissue from magnetic resonance images that is a combination of three existing techniques from the computer vision literature: expectation/maximization segmentation, binary mathematical morphology, and active contour models. Each of these techniques has been customized for the problem of brain tissue segmentation such that the resultant method is more robust than its components. Finally, we present the results of a parallel implementation of this method on IBM's supercomputer Power Visualization System for a database of 20 brain scans each with 256 x 256 x 124 voxels and validate those results against segmentations generated by neuroanatomy experts.