Region-based brain selection and classification on pet images for Alzheimer's disease computer aided diagnosis

Abstract

Positron Emission Tomography (PET) is a 3-D functional imaging modality which help physicians to diagnose neurodegenerative diseases like Alzheimer's Disease (AD). Computer-aided detection and diagnosis, based on medical imaging techniques is of importance for a quantitative evaluation. A novel method of ranking the effectiveness of brain regions to separate AD from healthy brains images is presented. Brain images are first mapped into 116 anatomical regions of interest. The first four moments and the entropy of the histograms of these regions are computed. Receiver Operating Characteristics curves are then used to rank the ability of regions to separate PET brain images. Twenty one selected regions are input to both Support Vector Machine and Random Forest classifiers and evaluation is done on 142 brain PET images. Classification results are better than those obtained when using the whole 116 initial regions or when inputting the whole brain voxels. In addition, an important computational time reduction was obtained.