Automated feature extraction from MRI brain scans and diagnosis of Alzheimer's disease are ongoing challenges. With advances in 3D imaging technology, 3D data acquisition is becoming more viable and efficient than its 2D counterpart. Rather than using feature-based vectors, in this paper, for the first time, we suggest a pipeline to extract novel covariance-based descriptors from the cortical surface using the Ricci energy optimization. The covariance descriptors are components of the nonlinear manifold of symmetric positive-definite matrices, thus we focus on using the Gaussian radial basis function to apply manifold-based classification to the 3D shape problem. Applying this novel signature to the analysis of abnormal cortical brain morphometry allows for diagnosing Alzheimer's disease. Experimental studies performed on about two hundred 3D MRI brain models, gathered from Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate the effectiveness of our descriptors in achieving remarkable classification accuracy.

翻译：从MRI脑扫描中自动提取特征并诊断阿尔茨海默病是当前持续挑战。随着三维成像技术的发展，三维数据采集相比二维手段更具可行性和效率。本文首次提出一种利用Ricci能量优化从皮层表面提取新型协方差描述子的流程，而非使用基于特征向量。协方差描述子是对称正定矩阵非线性流形的组成部分，因此我们重点采用高斯径向基函数将基于流形的分类方法应用于三维形状问题。将该新型签注应用于异常脑皮层形态测量分析，可实现阿尔茨海默病的诊断。基于阿尔茨海默病神经影像学计划（ADNI）数据集收集的约两百个三维MRI脑模型进行的实验研究表明，我们的描述子在实现显著分类准确率方面具有有效性。