Brain extraction from cerebral MRI volume using a hybrid level set based active contour neighborhood model.
Brain extraction from cerebral MRI volume using a hybrid level set based active contour neighborhood model.
复制标题
使用基于混合水平集的活动轮廓邻域模型从脑 MRI 体积中提取大脑
DOI：
10.1186/1475-925x-12-31
复制
发表时间：
2013-04-12
影响因子：
3.9
通讯作者：
Chen Z
中科院分区：
文献类型：
--
作者：
Jiang S;Zhang W;Wang Y;Chen Z
从脑MRI体积中提取脑组织是神经图像分析的重要前处理过程。作者提出了一种基于混合水平集的活动轮廓邻域模型的精确、鲁棒的脑提取方法。该方法在混合水平集模型中引入非线性速度函数，以消除边界泄漏。当使用新的混合水平集模型时，在大脑边界的邻域内迭代地应用活动轮廓邻域模型。提出了一种逐层轮廓初始化的方法来获取大脑边界的邻域。将该方法应用于互联网脑分割库（IBSR）提供的互联网脑MRI数据。在测试中，当在IBSR数据集（18 × 1.5 mm扫描）上执行我们的方法时，获得的平均Dice相似系数为0.95±0.02，平均Hausdorff距离为12.4±4.5。使用我们的方法得到的结果是非常相似的，使用手动分割，并实现了最小的平均豪斯多夫距离的IBSR数据。实现了从脑MRI体积中自动提取脑的方法，并产生了具有竞争力的准确结果。
The extraction of brain tissue from cerebral MRI volume is an important pre-procedure for neuroimage analyses. The authors have developed an accurate and robust brain extraction method using a hybrid level set based active contour neighborhood model. The method uses a nonlinear speed function in the hybrid level set model to eliminate boundary leakage. When using the new hybrid level set model an active contour neighborhood model is applied iteratively in the neighborhood of brain boundary. A slice by slice contour initial method is proposed to obtain the neighborhood of the brain boundary. The method was applied to the internet brain MRI data provided by the Internet Brain Segmentation Repository (IBSR). In testing, a mean Dice similarity coefficient of 0.95±0.02 and a mean Hausdorff distance of 12.4±4.5 were obtained when performing our method across the IBSR data set (18 × 1.5 mm scans). The results obtained using our method were very similar to those produced using manual segmentation and achieved the smallest mean Hausdorff distance on the IBSR data. An automatic method of brain extraction from cerebral MRI volume was achieved and produced competitively accurate results.