  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.
