Automated segmentation of multispectral brain MR images
Automated segmentation of multispectral brain MR images
复制标题
DOI：
10.1016/s0165-0270(02)00273-x
复制
发表时间：
2002-12-31
影响因子：
3
通讯作者：
Gash, DM
中科院分区：
文献类型：
--
作者：
Andersen, AH;Zhang, ZM;Gash, DM
这项工作提出了一种稳健且全面的方法，用于从多光谱磁共振成像（MRI）数据中对正常大脑组成进行体内自动分割和定量组织体积测量。基于有限混合模型的统计模式识别方法被用于将颅内容积分割为灰质（GM）、白质（WM）和脑脊液（CSF）空间。一种掩蔽算法首先从周围的脑膜外组织中提取脑体积。然后使用一种适应内在局部组织对比度的递归方法去除图像中的射频（RF）场不均匀性影响。我们的技术支持具有不同对比度和强度加权的多光谱MR图像的异构数据，这些图像是在不同的空间分辨率和方向上获取的。所提出的图像分割方法已经使用来自年轻和年老的非人灵长类动物以及人类受试者的多光谱T1加权、质子密度加权和T2加权MRI数据进行了测试。（C）2002年由爱思唯尔科学出版社（Elsevier Science B.V.）出版
This work presents a robust and comprehensive approach for the in vivo automated segmentation and quantitative tissue volume measurement of normal brain composition from multispectral magnetic resonance imaging (MRI) data. Statistical pattern recognition methods based on a finite mixture model are used to partition the intracranial volume into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) spaces. A masking algorithm initially extracts the brain volume from surrounding extrameningeal tissue. Radio frequency (RF) field inhomogeneity effects in the images are then removed using a recursive method that adapts to the intrinsic local tissue contrast. Our technique supports heterogeneous data with multispectral MR images of different contrast and intensity weighting acquired at varying spatial resolution and orientation. The proposed image segmentation methods have been tested using multispectral T1-, proton density-, and T2-weighted MRI data from young and aged non-human primates as well as from human subjects. (C) 2002 Published by Elsevier Science B.V.