Positron Emission Tomography (PET) imaging is of importance for diagnosing neurodegenerative diseases like Alzheimer Disease (AD). Computer-aided diagnosis methods could process and analyze quantitatively these images, in order to better characterize and extract meaningful information for medical diagnosis. This paper presents a novel computer-aided diagnosis technique for brain PET images classification in the case of AD. Brain images are first segmented into 116 Regions Of Interest (ROI) using an atlas. Computing some statistical features (mean, standard-deviation, skewness, kurtosis and entropy) on these regions' histogram, we define a Separation Power Factor (SPF) associated to each region. This factor quantifies the ability of each region to separate AD from Healthy Control (HC) brain images. Ranking selected regions according to their SPF and inputting them to a Support Vector Machine (SVM) classifier, yields the same or slightly better classification results than when inputting the whole brain voxels or the 116 regions, but with less computational time. Comparison is also done using Random Forest classifier.