Eldeeb Ghaidaa W, Zayed Nourhan, Yassine Inas A
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:57-60. doi: 10.1109/EMBC.2018.8512203.
Diffusion tensor imaging (DTI) has recently been added to the large scale of studies for Alzheimer's Disease (AD) to investigate the White Matter (WM) defects that are not detectable using structural MRI. In this paper, we extracted Speeded Up Robust Features (SURF) and Scale Invariant Feature Transform (SIFT) features, based on the visual diffusion patterns of Fractional Anisotropy (FA), and Mean Diffusivity (MD) maps, to build bag-of-words AD-signature for the hippocampal area. The experiments were accomplished with a subset of participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset formed of AD patients (n = 35), Early Mild Cognitive Impairment (EMCI) (n=6), Late Mild Cognitive Impairment (LMCI) (n=24) and cognitively healthy elderly Normal Controls (NC) (n=31). The preliminary studied experiments give promising results that would consider the proposed system as an accurate and useful tool to capture the AD leanness with accuracy of 87% and 89% for FA and MD maps respectively.
扩散张量成像（DTI）最近被纳入了对阿尔茨海默病（AD）的大规模研究中，以探究使用结构磁共振成像（MRI）无法检测到的白质（WM）缺陷。在本文中，我们基于分数各向异性（FA）和平均扩散率（MD）图的视觉扩散模式，提取了加速稳健特征（SURF）和尺度不变特征变换（SIFT）特征，以构建海马区的词袋AD特征。实验使用了来自阿尔茨海默病神经影像倡议（ADNI）数据集的一部分参与者完成，该数据集由AD患者（n = 35）、早期轻度认知障碍（EMCI）（n = 6）、晚期轻度认知障碍（LMCI）（n = 24）和认知健康的老年正常对照（NC）（n = 31）组成。初步研究实验给出了有前景的结果，这将使所提出的系统被视为一种准确且有用的工具，分别以87%和89%的准确率捕捉FA和MD图的AD特征。
