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Classification of ADNI PET images via regularized 3D functional data analysis

ABSTRACT We propose a penalized Haar wavelet approach for the classification of three-dimensional (3D) brain images in the framework of functional data analysis, which treats each entire 3D brain image as a single functional input, thus automatically takes into account the spatial correlations of voxel-level imaging measures. We validate the proposed approach through extensive simulations and compare its classification performance with other commonly used machine learning methods, which show that the proposed method outperforms other methods in both classification accuracy and identification of the relevant voxels. We then apply the proposed method to the practical classification problems for Alzheimer's disease using positron emission tomography images obtained from the Alzheimer's Disease Neuroimaging Initiative database to highlight the advantages of our approach.