Systems and Biomedical Engineering, Cairo University, Cairo, Egypt.
Computer and Systems Department, Electronics Research Institute, Cairo, Egypt.
Sci Rep. 2023 Aug 23;13(1):13734. doi: 10.1038/s41598-023-40635-2.
Alzheimer's disease (AD) is considered one of the most spouting elderly diseases. In 2015, AD is reported the US's sixth cause of death. Substantially, non-invasive imaging is widely employed to provide biomarkers supporting AD screening, diagnosis, and progression. In this study, Gaussian descriptors-based features are proposed to be efficient new biomarkers using Magnetic Resonance Imaging (MRI) T-weighted images to differentiate between Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and Normal controls (NC). Several Gaussian map-based features are extracted such as Gaussian shape operator, Gaussian curvature, and mean curvature. The aforementioned features are then introduced to the Support Vector Machine (SVM). They were, first, calculated separately for the Hippocampus and Amygdala. Followed by the fusion of the features. Moreover, Fusion of the regions before feature extraction was also employed. Alzheimer's disease Neuroimaging Initiative (ADNI) dataset, formed of 45, 55, and 65 cases for AD, MCI, and NC respectively, is appointed in this study. The shape operator feature outperformed the other features, with 74.6%, and 98.9% accuracy in the case of normal vs. abnormal, and AD vs. MCI classification respectively.
阿尔茨海默病（AD）被认为是最常见的老年疾病之一。2015 年，AD 是美国第六大死因。大量研究表明，非侵入性成像技术广泛应用于提供支持 AD 筛查、诊断和进展的生物标志物。在这项研究中，我们提出了基于高斯描述符的特征，利用磁共振成像（MRI）T 加权图像，作为区分阿尔茨海默病（AD）、轻度认知障碍（MCI）和正常对照组（NC）的有效新型生物标志物。提取了几种基于高斯图的特征，如高斯形状算子、高斯曲率和平均曲率。然后将上述特征引入支持向量机（SVM）中。首先，它们分别为海马体和杏仁核计算。然后是特征融合。此外，还采用了特征提取前的区域融合。本研究采用了由 45、55 和 65 例 AD、MCI 和 NC 组成的阿尔茨海默病神经影像学倡议（ADNI）数据集。在正常与异常、AD 与 MCI 分类的情况下，形状算子特征的准确率分别为 74.6%和 98.9%，优于其他特征。
