Alternative feature extraction methods in 3D brain image-based diagnosis of Alzheimer's Disease

Positron Emission Tomography plays an important role as an Alzheimer's Disease (AD) early diagnosis tool, and also identifying Mild Cognitive Impairment (MCI) patients. The vast majority of 3D brain image-based computer aided diagnosis methods implemented so far relied simply on voxel intensity, as feature. In this article, we consider two alternative methods of feature extraction: 3D Haar-like features and histograms of gradient magnitude and orientation; their performance in the classification of AD vs. Cognitively Normal (CN), MCI vs. CN and AD vs. MCI patients is evaluated and compared to the one obtained when using voxel intensity only. Classification is accomplished through Support Vector Machines, after an automatic feature selection step. The features based on histograms of the gradient attained the best results in AD vs. CN discrimination, and 3D Haar-like features improved performance in all three classification tasks. These improvements encourage further investigation on these extraction strategies.