Using volume features and shape features for Alzheimer's disease diagnosis

The way that Alzheimer's disease (AD) invades brain is to destroy its fundamental elements, i.e. neurons. The phenomenon of neuron destruction reflects volume changes on brain tissues such as gray matter, white matter and cerebro-spinal fluid. In the AD-related research, the volumetric analysis of hippocampus is the most extensive study. However, the segmentation and identification of the hippocampus are highly complicated and time-consuming. Therefore, we designed a MRI-based classification framework to distinguish AD's patients from normal individuals. First, 3-D volumetric features and 2-D shape features were extracted from MRI data. Afterward, Principle component analysis (PCA) was utilized to decrease the dimensions of feature space. Finally, a SVM classifier was trained for AD classification. With the proposed framework, the classification accuracy is improved from 64% or 72%, by only using 3-D volumetric features or 2-D shape features, to 84% by using both features.