Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 6, 2026

Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451
05:17

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

Medical Physics
|December 17, 2010
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Latent Space Projections and Atlases, a Cautionary Tale in Deep Neuroimaging using Autoencoders.

International journal of neural systems·2026
Same author

An explainable framework for the relationship between dementia and metabolism patterns.

NeuroImage·2026
Same author

Management of extensively drug-resistant <i>Acinetobacter baumannii</i> bacteremia with cefiderocol and sulbactam-durlobactam: a case report.

ASM case reports·2025
Same author

Statistical agnostic regression: A machine learning method to validate regression models.

Journal of advanced research·2025
Same author

Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson's Disease Using Multimodal Data.

International journal of neural systems·2023
Same author

A Connection Between Pattern Classification by Machine Learning and Statistical Inference With the General Linear Model.

IEEE journal of biomedical and health informatics·2021

This study introduces a computer-aided diagnosis method for early Alzheimer's disease (AD) detection. The technique achieves high accuracy, outperforming other methods in classifying normal controls, mild cognitive impairment, and AD subjects.

Area of Science:

  • Medical Imaging
  • Neurology
  • Computer-Aided Diagnosis

Background:

  • Alzheimer's disease (AD) diagnosis relies on accurate identification of neurodegenerative changes.
  • Early detection of AD is crucial for timely intervention and management.
  • 18F-FDG PET imaging offers insights into brain metabolism, valuable for AD assessment.

Purpose of the Study:

  • To develop and evaluate a computer-aided diagnosis (CAD) technique for enhancing early Alzheimer's disease (AD) detection accuracy.
  • To analyze 18F-FDG PET images from normal controls (NC), mild cognitive impairment (MCI), and AD subjects.
  • To compare the proposed CAD technique against existing methods for classification performance.

Main Methods:

  • Voxel selection using t-tests and feature dimension reduction via factor analysis.

Related Experiment Videos

Last Updated: Jun 6, 2026

Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451
05:17

Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451

Published on: April 18, 2025

  • Utilizing factor loadings as features for classification.
  • Employing three classifiers: Gaussian mixture models (linear and quadratic discriminant functions) and a support vector machine (SVM) with a linear kernel.

    • Main Results:

    • Achieved up to 95% accuracy in classifying normal controls (NC) versus Alzheimer's disease (AD) subjects.
    • Obtained 88% accuracy for NC-MCI classification and 86% for NC-MCI-AD classification using SVM with a linear kernel.
    • Demonstrated superior classification performance compared to voxel-as-features and PCA-based approaches.

    Conclusions:

    • The proposed computer-aided diagnosis methodology shows significant potential for accurate early Alzheimer's disease detection.
    • The factor analysis-based feature extraction combined with SVM offers improved classification accuracy.
    • This technique provides a promising tool for differentiating between normal cognition, mild cognitive impairment, and Alzheimer's disease stages.