Kapur T, Grimson W E, Wells W M, Kikinis R
Massachusetts Institute of Technology, Artificial Intelligence Laboratory, Cambridge 02139, USA.
Med Image Anal. 1996 Jun;1(2):109-27. doi: 10.1016/S1361-8415(96)80008-9.
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.
由于医学图像的复杂性，以及缺乏能够完全捕捉每个结构中可能变形的解剖模型，医学图像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，其分割对于许多问题来说都是重要的一步，包括形态学时间变化检测研究以及手术规划的三维可视化。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、二值数学形态学和活动轮廓模型。这些技术中的每一种都针对脑组织分割问题进行了定制，以使所得方法比其各个组成部分更稳健。最后，我们展示了该方法在IBM超级计算机Power Visualization System上针对一个包含20个脑部扫描的数据库的并行实现结果，每个扫描有256 x 256 x 124体素，并将这些结果与神经解剖学专家生成的分割结果进行了验证。
