Zhuang Audrey H, Valentino Daniel J, Toga Arthur W
Laboratory of Neuroimaging, Department of Neurology, University of California-Los Angeles, Los Angeles, CA 90095, USA.
Neuroimage. 2006 Aug 1;32(1):79-92. doi: 10.1016/j.neuroimage.2006.03.019. Epub 2006 May 11.
The segmentation of brain tissue from nonbrain tissue in magnetic resonance (MR) images, commonly referred to as skull stripping, is an important image processing step in many neuroimage studies. A new mathematical algorithm, a model-based level set (MLS), was developed for controlling the evolution of the zero level curve that is implicitly embedded in the level set function. The evolution of the curve was controlled using two terms in the level set equation, whose values represented the forces that determined the speed of the evolving curve. The first force was derived from the mean curvature of the curve, and the second was designed to model the intensity characteristics of the cortex in MR images. The combination of these forces in a level set framework pushed or pulled the curve toward the brain surface. Quantitative evaluation of the MLS algorithm was performed by comparing the results of the MLS algorithm to those obtained using expert segmentation in 29 sets of pediatric brain MR images and 20 sets of young adult MR images. Another 48 sets of elderly adult MR images were used for qualitatively evaluating the algorithm. The MLS algorithm was also compared to two existing methods, the brain extraction tool (BET) and the brain surface extractor (BSE), using the data from the Internet brain segmentation repository (IBSR). The MLS algorithm provides robust skull-stripping results, making it a promising tool for use in large, multi-institutional, population-based neuroimaging studies.
在磁共振（MR）图像中，将脑组织与非脑组织进行分割，通常称为去颅骨，这是许多神经影像研究中重要的图像处理步骤。开发了一种新的数学算法——基于模型的水平集（MLS），用于控制隐式嵌入水平集函数中的零水平曲线的演化。曲线的演化通过水平集方程中的两项来控制，这两项的值代表决定演化曲线速度的力。第一个力源自曲线的平均曲率，第二个力旨在对MR图像中皮层的强度特征进行建模。在水平集框架中，这些力的组合将曲线推向或拉向脑表面。通过将MLS算法的结果与在29组儿科脑MR图像和20组年轻成人MR图像中使用专家分割获得的结果进行比较，对MLS算法进行了定量评估。另外48组老年成人MR图像用于对该算法进行定性评估。还使用来自互联网脑分割库（IBSR）的数据，将MLS算法与两种现有方法——脑提取工具（BET）和脑表面提取器（BSE）进行了比较。MLS算法提供了稳健的去颅骨结果，使其成为用于大型、多机构、基于人群的神经影像研究的有前途的工具。
