Automatic selection of ROIs in functional imaging using Gaussian mixture models
Automatic selection of ROIs in functional imaging using Gaussian mixture models
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DOI：
10.1016/j.neulet.2009.05.039
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发表时间：
2009-08-28
影响因子：
2.5
通讯作者：
Lang, E. W.
中科院分区：
文献类型：
--
作者：
Gorriz, J. M.;Lassl, A.;Lang, E. W.
我们提出了一种使用高斯混合模型（GMM）来选择三维功能性大脑图像中包含的信息的感兴趣区域（ROI）的自动方法，其中每个高斯模型都包含具有相似激活的连续大脑区域。该方法的新颖之处在于通过高斯函数之和来近似大脑图像的灰度分布，其参数由期望最大化（EM）算法的最大似然标准确定。每个高斯或簇都由具有中心坐标和特定形状的多元高斯函数表示。这种方法导致大脑图像中包含的信息被大幅压缩，并作为用于诊断大脑疾病的各种可能的特征提取方法的起点。 (C) 2009 Elsevier Ireland Ltd. 保留所有权利。
We present an automatic method for selecting regions of interest (ROIs) of the information contained in three-dimensional functional brain images using Gaussian mixture models (GMMs), where each Gaussian incorporates a contiguous brain region with similar activation. The novelty of the approach is based on approximating the grey-level distribution of a brain image by a sum of Gaussian functions, whose parameters are determined by a maximum likelihood criterion via the expectation maximization (EM) algorithm. Each Gaussian or cluster is represented by a multivariate Gaussian function with a center coordinate and a certain shape. This approach leads to a drastic compression of the information contained in the brain image and serves as a starting point for a variety of possible feature extraction methods for the diagnosis of brain diseases. (C) 2009 Elsevier Ireland Ltd. All rights reserved.