School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China.
J Clin Neurophysiol. 2012 Jun;29(3):278-86. doi: 10.1097/WNP.0b013e3182570f94.
The aim was to automatically segment brain magnetic resonance (MR) image with inhomogeneous and partial volume (PV) intensity for brain and neurophysiology analysis.
Rather than assuming the presence of a single bias field over the image data, we first apply a local model to MR image analysis. With the brain topology knowledge, several specific local regions are selected, and typical brain tissues are then extracted for the prior estimation of fuzzy clustering center and member function. A new nonlocal fuzzy labeling scheme is applied to global optimization segmentation based on the block comparison and distance weight, which is robust to noise and inhomogeneous intensity. The nonlocal labeling provides optimized fuzzy member value and local intensity estimation of brain tissues such as cerebrospinal fluid (CSF), white matter (WM), and gray matter (GM). In addition to inhomogeneous intensity, PV may lead to error segmentation. To correct error segmentation because of PV, this article also provides two correction schemes. The first one is to extract CSF in deep sulci, which captures more CSF candidate by intensity comparison and topology shape comparison. The local pure CSF, WM, and GM is then estimated to correct the interfaces of CSF/GM and WM/GM.
The segmentation experiments are performed on both brainweb-simulated images and Internet brain segmentation repository database (IBSR) real images. The experimental results demonstrate the robust and efficient performance of our approach.
Our approach can be applied to automatic segmentation of the brain MR image.
旨在自动分割脑磁共振（MR）图像中的不均匀和部分容积（PV）强度，用于脑和神经生理学分析。
我们首先应用局部模型进行磁共振图像分析，而不是假设图像数据中存在单一的偏置场。利用脑拓扑知识，选择几个特定的局部区域，然后提取典型的脑组织，用于模糊聚类中心和成员函数的先验估计。基于块比较和距离权重的全局优化分割应用新的非局部模糊标记方案，对噪声和不均匀强度具有鲁棒性。非局部标记提供了优化的模糊成员值和脑组织（如脑脊液（CSF）、白质（WM）和灰质（GM））的局部强度估计。除了不均匀强度外，PV 还可能导致错误分割。为了纠正由于 PV 导致的错误分割，本文还提供了两种校正方案。第一种是在深沟中提取 CSF，通过强度比较和拓扑形状比较捕获更多的 CSF 候选物。然后估计局部纯 CSF、WM 和 GM，以校正 CSF/GM 和 WM/GM 的界面。
在脑模拟图像和互联网脑分割数据库（IBSR）真实图像上进行了分割实验。实验结果证明了我们方法的稳健和高效性能。
我们的方法可应用于脑磁共振图像的自动分割。
