Related Experiment Video
Updated: Jun 6, 2026
Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451
Published on: April 18, 2025
Feature selection using factor analysis for Alzheimer's diagnosis using 18F-FDG PET images
D Salas-Gonzalez1, J M Górriz, J Ramírez
1Department of Signal Theory, Networking and Communications, ETSIIT 18071, University of Granada, Granada, Spain. dsalas@ugr.es
Purpose:
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.
Methods:
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.
Results:
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.
Conclusions:
Results are compared to the voxel-as-features and a PCA- based approach and the proposed methodology achieves better classification performance.