Ji Zexuan, Xia Yong, Sun Quansen, Chen Qiang, Xia Deshen, Feng David Dagan
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China.
IEEE Trans Inf Technol Biomed. 2012 May;16(3):339-47. doi: 10.1109/TITB.2012.2185852. Epub 2012 Jan 24.
Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of noise and intensity inhomogeneity in brain MR images, many segmentation algorithms suffer from limited accuracy. In this paper, we assume that the local image data within each voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM) algorithm for automated brain MR image segmentation. This algorithm estimates the segmentation result that maximizes the posterior probability by minimizing an objective energy function, in which a truncated Gaussian kernel function is used to impose the spatial constraint and fuzzy memberships are employed to balance the contribution of each GMM. We compared our algorithm to state-of-the-art segmentation approaches in both synthetic and clinical data. Our results show that the proposed algorithm can largely overcome the difficulties raised by noise, low contrast, and bias field, and substantially improve the accuracy of brain MR image segmentation.
从磁共振（MR）图像中准确分割脑组织是定量脑图像分析中的关键步骤。然而，由于脑MR图像中存在噪声和强度不均匀性，许多分割算法的准确性有限。在本文中，我们假设每个体素邻域内的局部图像数据满足高斯混合模型（GMM），因此提出了用于自动脑MR图像分割的模糊局部GMM（FLGMM）算法。该算法通过最小化一个目标能量函数来估计使后验概率最大化的分割结果，其中使用截断高斯核函数施加空间约束，并采用模糊隶属度来平衡每个GMM的贡献。我们将我们的算法与合成数据和临床数据中的最新分割方法进行了比较。我们的结果表明，所提出的算法可以很大程度上克服由噪声、低对比度和偏置场带来的困难，并显著提高脑MR图像分割的准确性。
