This work presents a novel feature selection method for Positron Emission Tomography (PET) images for the development of a computer aided diagnosis (CAD) system aiming to improve the early detection of the Alzheimer’s Disease (AD). Voxels are selected by means of the combination of the antecedents and consequents of Association Rules (ARs) which are mined from the Regions of Interest (ROIs) of controls by Apriori algorithm. In order to reduce the input space to the classifier, features are extracted using Principal Component Analysis (PCA) or Partial Least Squares (PLS) techniques. Finally, classification is performed by using a kernel Support Vector Machine (SVM) reaching accuracy rates of 90% outperforming other reported methods.
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