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 Concept Videos

Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

1.5K
Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
1.5K
Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

707
Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
707
Dementia01:30

Dementia

459
Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
The progression of dementia is generally gradual....
459
Classification of Illness01:17

Classification of Illness

8.4K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.4K
Aggregates Classification01:29

Aggregates Classification

915
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
915
Seizures: Classification01:13

Seizures: Classification

1.2K
Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
1.2K

You might also read

Related Articles

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

Sort by
Same author

Synthetic data augmentation for improving performance in deep learning models for anatomical landmark localization on the distal upper limb.

Frontiers in bioengineering and biotechnology·2026
Same author

Scattering Coefficient Estimation Using Thin-Film Phantoms with a Spectral-Domain Dental OCT System.

Sensors (Basel, Switzerland)·2026
Same author

MetaAcuPoint: MetaHuman-Generated Synthetic Data for Hand Acupoint Localization.

Healthcare (Basel, Switzerland)·2025
Same author

Advancing Medical Training with Mixed Reality and Haptic Feedback Simulator for Acupuncture Needling.

Sensors (Basel, Switzerland)·2025
Same author

Multi-Channel Spectro-Temporal Representations for Speech-Based Parkinson's Disease Detection.

Journal of imaging·2025
Same author

Structure-Preserving Histopathological Stain Normalization via Attention-Guided Residual Learning.

Bioengineering (Basel, Switzerland)·2025

Related Experiment Video

Updated: Dec 30, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.6K

Alzheimer's Disease Classification Based on Multi-feature Fusion.

Nuwan Madusanka1, Heung-Kook Choi1, Jae-Hong So2

  • 1Department of Computer Engineering, u-AHRC, Inje University, Gimhae, Gyeongsangnam, Korea.

Current Medical Imaging Reviews
|January 25, 2020
PubMed
Summary

This study fuses texture and morphometric features from brain MR images to diagnose Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI). The approach shows promise for accurate classification of AD, MCI, and Normal Control (NC) subjects.

Keywords:
Alzheimer's diseaseMCIatrophyclassificationcognitive symptomsneurodegenerative diseases

More Related Videos

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

717
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.2K

Related Experiment Videos

Last Updated: Dec 30, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.6K
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

717
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.2K

Area of Science:

  • Neuroimaging
  • Biomarker Discovery
  • Machine Learning in Medicine

Background:

  • Alzheimer's Disease (AD) diagnosis relies on neuropsychiatric evaluation and imaging data.
  • Cerebral atrophy in Magnetic Resonance (MR) images correlates with cognitive decline, making them crucial for AD diagnosis.
  • Current diagnostic methods can be enhanced by identifying reliable biomarkers.

Purpose of the Study:

  • To investigate the fusion of texture and morphometric features as a diagnostic biomarker for Alzheimer's Disease (AD).
  • To classify subjects into Alzheimer's disease, Mild Cognitive Impairment (MCI), and Normal Control (NC) groups.
  • To evaluate the efficacy of combining different feature types for improved diagnostic accuracy.

Main Methods:

  • Utilized structural MR images to extract Gabor, hippocampus morphometric, and Gray Level Co-occurrence Matrix (GLCM) features (2D and 3D).
  • Employed a 5-fold cross-validated Support Vector Machine (SVM) classifier.
  • Implemented multi-feature fusion approaches using 2DGLCM and 3DGLCM.

Main Results:

  • Achieved high classification rates for AD vs. NC: 81.05% (2DGLCM) and 86.61% (3DGLCM) correct classification.
  • Demonstrated strong sensitivity and specificity for AD vs. NC classification, outperforming existing methods.
  • Showcased accurate classification for MCI vs. AD and MCI vs. NC groups using multiclass SVM.

Conclusions:

  • The fusion of texture and morphometric features from MR images is an effective strategy for AD and MCI diagnosis.
  • The proposed approach offers a promising avenue for enhancing the accuracy of classifying AD, MCI, and NC subjects.
  • This method holds potential for clinical application in early and accurate diagnosis of neurodegenerative conditions.