School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore.
Neuroimage. 2010 Jan 1;49(1):225-39. doi: 10.1016/j.neuroimage.2009.08.050. Epub 2009 Sep 2.
Removal of non-brain tissues, particularly dura, is an important step in enabling accurate measurement of brain structures. Many popular methods rely on iterative surface deformation to fit the brain boundary and tend to leave residual dura. Similar to other approaches, the method proposed here uses intensity thresholding followed by removal of narrow connections to obtain a brain mask. However, instead of using morphological operations to remove narrow connections, a graph theoretic image segmentation technique was used to position cuts that isolate and remove dura. This approach performed well on both the standardized IBSR test data sets and empirically derived data. Compared to the Hybrid Watershed Algorithm (HWA; (Segonne et al., 2004)) the novel approach achieved an additional 10-30% of dura removal without incurring further brain tissue erosion. The proposed method is best used in conjunction with HWA as the errors produced by the two approaches often occur at different locations and cancel out when their masks are combined. Our experiments indicate that this combination can substantially decrease and often fully avoid cortical surface overestimation in subsequent segmentation.
去除非脑组织，特别是硬脑膜，是实现脑结构精确测量的重要步骤。许多流行的方法都依赖于迭代表面变形来拟合脑边界，并且往往会留下残余的硬脑膜。与其他方法类似，这里提出的方法使用强度阈值化，然后去除狭窄的连接，以获得脑掩模。然而，与使用形态操作去除狭窄连接不同，使用图论图像分割技术来定位切割，以隔离和去除硬脑膜。该方法在标准化的 IBSR 测试数据集和经验衍生数据上都表现良好。与 Hybrid Watershed Algorithm（HWA；（Segonne et al.，2004））相比，该新方法在不进一步侵蚀脑组织的情况下，额外去除了 10-30%的硬脑膜。该方法最好与 HWA 结合使用，因为两种方法产生的误差通常出现在不同的位置，并且在它们的掩模组合时会相互抵消。我们的实验表明，这种组合可以大大减少，并且通常可以完全避免后续分割中皮质表面的高估。
