Beheshti Iman, Demirel Hasan
Biomedical Image Processing Lab, Department of Electrical & Electronic Engineering, Eastern Mediterranean University, Gazimagusa, Mersin 10, Turkey.
Magn Reson Imaging. 2016 Apr;34(3):252-63. doi: 10.1016/j.mri.2015.11.009. Epub 2015 Dec 3.
High-dimensional classification approaches have been widely used to investigate magnetic resonance imaging (MRI) data for automatic classification of Alzheimer's disease (AD). This paper describes the use of t-test based feature-ranking approach as part of a novel feature selection procedure, where the number of top features is determined using the Fisher Criterion. The proposed classification system involves five systematic levels. First, voxel-based morphometry technique is used to compare the global and local differences of gray matter in patients with AD versus healthy controls (HCs). The significant local differences in gray matter volume are then selected as volumes of interests (VOIs). Second, the voxel clusters are employed as VOIs, where each voxel is considered to be a feature. Third, all the features are ranked using t-test scores. In this regard, the Fisher Criterion between the AD and HC groups is calculated for a changing number of ranked features, where the vector size maximizing the Fisher Criterion is selected as the optimal number of top discriminative features. Fourth, the classification is performed using support vector machine. Finally, data fusion methods among atrophy clusters are used to improve the classification performance. The experimental results indicate that the performance of the proposed system could compete well with the state-of-the-art techniques reported in the literature.
高维分类方法已被广泛用于研究磁共振成像（MRI）数据，以实现阿尔茨海默病（AD）的自动分类。本文描述了基于t检验的特征排序方法的使用，作为一种新颖的特征选择过程的一部分，其中顶级特征的数量使用Fisher准则来确定。所提出的分类系统包括五个系统级别。首先，基于体素的形态测量技术用于比较AD患者与健康对照（HC）之间灰质的全局和局部差异。然后将灰质体积的显著局部差异选为感兴趣区域（VOI）。其次，将体素簇用作VOI，其中每个体素被视为一个特征。第三，使用t检验分数对所有特征进行排序。在这方面，针对排序特征数量的变化计算AD组和HC组之间的Fisher准则，其中使Fisher准则最大化的向量大小被选为顶级判别特征的最佳数量。第四，使用支持向量机进行分类。最后，使用萎缩簇之间的数据融合方法来提高分类性能。实验结果表明，所提出系统的性能可以与文献中报道的最先进技术相媲美。
