Alzheimer's disease is a type of dementia that mainly affects elderly people, with unknown causes and no effective treatment up to date. The diagnosis of this disease in an earlier stage is crucial to improve patients' life quality. Current techniques focus on the analysis of neuroimages, like FDG-PET or MRI, to find changes in the brain activity. While high accuracies can be obtained by combining the analysis of several types of neuroimages, they are expensive and not always available for medical analysis. Achieving similar results using only 3-D FDG-PET scans is therefore of huge importance. While directly applying classifiers to the FDG-PET scan voxel intensities can lead to good prediction accuracies, it results in a problem that suffers from the curse of dimensionality. This paper thus proposes a methodology to identify regions of interest by segmenting 3-D FDG-PET scans and extracting features that represent each of those regions of interest, reducing the dimensionality of the space. Experimental results show that the proposed methodology outperforms the one using voxel intensities despite only a small number of features is needed to achieve that result.