Sub-cortical shape morphology and voxel-based features for Alzheimer's disease classification

摘要

Neurodegenerative pathologies, such as Alzheimer's disease, are linked with morphological alterations and tissue variations in sub-cortical structures which can be assessed from medical imaging and biological data. In this work, we present an unsupervised framework for the classification of Alzheimer's disease (AD) patients, stratifying patients into four diagnostic groups, namely: AD, early Mild Cognitive Impairment (MCI), late MCI and normal controls by combining shape and voxel-based features from 12 sub-cortical areas. An automated anatomical labeling using an atlas-based segmentation approach is proposed to extract multiple regions of interest known to be linked with AD progression. We take advantage of gray-matter voxel-based intensity variations and structural alterations extracted with a spherical harmonics framework to learn the discriminative features between multiple diagnostic classes. The proposed method is validated on 600 patients from the ADNI database by training binary SVM classifiers of dimensionality reduced features, using both linear and RBF kernels. Results show near state-of-the-art approaches in classification accuracy (>88%), especially for the more challenging discrimination tasks: AD vs. LMCI (76.81%), NC vs. EMCI (75.46%) and EMCI vs. LMCI (70.95%). By combining multimodality features, this pipeline demonstrates the potential by exploiting complementary features to improve cognitive assessment.