Moraru Luminita, Moldovanu Simona, Dimitrievici Lucian Traian, Dey Nilanjan, Ashour Amira S, Shi Fuqian, Fong Simon James, Khan Salam, Biswas Anjan
Faculty of Sciences and Environment, Modelling & Simulation Laboratory, Dunarea de Jos University of Galati, 47 Domneasca Str., 800008, Romania.
Department of Computer Science and Engineering, Electrical and Electronics Engineering, Faculty of Control Systems, Computers, Dunarea de Jos University of Galati, Romania.
J Adv Res. 2019 Jan 4;16:15-23. doi: 10.1016/j.jare.2019.01.001. eCollection 2019 Mar.
A Gaussian mixture model (GMM)-based classification technique is employed for a quantitative global assessment of brain tissue changes by using pixel intensities and contrast generated by b-values in diffusion tensor imaging (DTI). A hemisphere approach is also proposed. A GMM identifies the variability in the main brain tissues at a macroscopic scale rather than searching for tumours or affected areas. The asymmetries of the mixture distributions between the hemispheres could be used as a sensitive, faster tool for early diagnosis. The k-means algorithm optimizes the parameters of the mixture distributions and ensures that the global maxima of the likelihood functions are determined. This method has been illustrated using 18 sub-classes of DTI data grouped into six levels of diffusion weighting (b = 0; 250; 500; 750; 1000 and 1250 s/mm) and three main brain tissues. These tissues belong to three subjects, i.e., healthy, multiple haemorrhage areas in the left temporal lobe and ischaemic stroke. The mixing probabilities or weights at the class level are estimated based on the sub-class-level mixing probability estimation. Furthermore, weighted Euclidean distance and multiple correlation analysis are applied to analyse the dissimilarity of mixing probabilities between hemispheres and subjects. The silhouette data evaluate the objective quality of the clustering. By using a GMM in the present study, we establish an important variability in the mixing probability associated with white matter and grey matter between the left and right hemispheres.
一种基于高斯混合模型（GMM）的分类技术被用于通过扩散张量成像（DTI）中b值产生的像素强度和对比度对脑组织变化进行定量全局评估。还提出了一种半球方法。GMM在宏观尺度上识别主要脑组织的变异性，而不是寻找肿瘤或受影响区域。半球之间混合分布的不对称性可作为早期诊断的敏感、快速工具。k均值算法优化混合分布的参数，并确保确定似然函数的全局最大值。使用分为六个扩散加权级别（b = 0；250；500；750；1000和1250 s/mm²）的18个子类DTI数据以及三种主要脑组织说明了该方法。这些组织属于三个受试者，即健康受试者、左侧颞叶有多个出血区域的受试者和缺血性中风受试者。基于子类级混合概率估计来估计类级的混合概率或权重。此外，应用加权欧几里得距离和多重相关性分析来分析半球和受试者之间混合概率的差异。轮廓数据评估聚类的客观质量。在本研究中通过使用GMM，我们确定了左右半球之间与白质和灰质相关的混合概率的重要变异性。
