A comparative study of feature extraction methods for the diagnosis of Alzheimer's disease using the ADNI database

Authors:
F. Segovia;J. M. Górriz;J. Ramírez;D. Salas-Gonzalez;I. Álvarez;M. López;R. Chaves
Affiliations:
Department of Signal Theory, Networking and Communications, University of Granada, Spain;Department of Signal Theory, Networking and Communications, University of Granada, Spain;Department of Signal Theory, Networking and Communications, University of Granada, Spain;Department of Signal Theory, Networking and Communications, University of Granada, Spain;Department of Signal Theory, Networking and Communications, University of Granada, Spain;Department of Signal Theory, Networking and Communications, University of Granada, Spain;Department of Signal Theory, Networking and Communications, University of Granada, Spain
Venue:
Neurocomputing
Year:
2012

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Abstract

Several approaches appear in literature in order to develop Computed-Aided-Diagnosis (CAD) systems for Alzheimer's disease (AD) detection. Although univariate models became very popular and nowadays they are widely used, recent investigations are focused on multivariate models which deal with a whole image as an observation. In this work, we compare two multivariate approaches that use different methodologies to relieve the small sample size problem. One of them is based on Gaussian Mixture Model (GMM) and models the Regions of Interests (ROIs) defined as differences between controls and AD subject. After GMM estimation using the EM algorithm, feature vectors are extracted for each image depending on the positions of the resulting Gaussians. The other method under study computes score vectors through a Partial Least Squares (PLS) algorithm based estimation and those vectors are used as features. Before extracting the score vectors, a binary mask based dimensional reduction of the input space is performed in order to remove low-intensity voxels. The validity of both methods is tested on the ADNI database by implementing several CAD systems with linear and nonlinear classifiers and comparing them with previous approaches such as VAF and PCA.