Send to

Choose Destination
See comment in PubMed Commons below
Med Phys. 2010 Nov;37(11):6084-95.

Feature selection using factor analysis for Alzheimer's diagnosis using 18F-FDG PET images.

Author information

  • 1Department of Signal Theory, Networking and Communications, ETSIIT 18071, University of Granada, Granada, Spain.



This article presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of Alzheimer's disease (AD). Two hundred and ten 18F-FDG PET images from the ADNI initiative [52 normal controls (NC), 114 mild cognitive impairment (MCI), and 53 AD subjects] are studied.


The proposed methodology is based on the selection of voxels of interest using the t-test and a posterior reduction of the feature dimension using factor analysis. Factor loadings are used as features for three different classifiers: Two multivariate Gaussian mixture model, with linear and quadratic discriminant function, and a support vector machine with linear kernel.


An accuracy rate up to 95% when NC and AD are considered and an accuracy rate up to 88% and 86% for NC-MCI and NC-MCI,AD, respectively, are obtained using SVM with linear kernel.


Results are compared to the voxel-as-features and a PCA- based approach and the proposed methodology achieves better classification performance.

[PubMed - indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for American Institute of Physics Icon for PubMed Central
    Loading ...
    Write to the Help Desk