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 Videos

Graphical-Model-based Morphometric Analysis.

Rong Chen1, Edward H Herskovits

  • 1Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA. rong.chen@uphs.upenn.edu

IEEE Transactions on Medical Imaging
|October 19, 2005
PubMed
Summary
This summary is machine-generated.

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

Changes in Brain Activation Patterns During Working Memory Tasks in People With Post-COVID Condition and Persistent Neuropsychiatric Symptoms.

Neurology·2023
Same author

Artificial intelligence in molecular imaging.

Annals of translational medicine·2021
Same author

Computational framework for detection of subtypes of neuropsychiatric disorders based on DTI-derived anatomical connectivity.

The neuroradiology journal·2020
Same author

Medical Image Analysis: Human and Machine.

Academic radiology·2019
Same author

Science to Practice: IT Solutions to Drive Standardized Report Recommendations for Abdominal Aortic Aneurysm Surveillance.

Journal of the American College of Radiology : JACR·2018
Same author

Multicenter Research Studies in Radiology.

Academic radiology·2017
Same journal

MUST: Multi-style virtual staining with incomplete pairs.

IEEE transactions on medical imaging·2026
Same journal

BrainCL: Transformer-Based Brain Network Contrastive Learning with Multi-Order Topology and Salience Masking.

IEEE transactions on medical imaging·2026
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
See all related articles

Graphical-Model-based Morphometric Analysis (GAMMA) automatically detects brain abnormalities using Bayesian networks and Markov random fields. This novel voxel-based morphometry method offers higher sensitivity and specificity than previous approaches for analyzing MRI data.

Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Computational Neuroscience

Background:

  • Voxel-based morphometry (VBM) is crucial for identifying brain structural changes.
  • Existing VBM methods may struggle with complex, nonlinear associations between brain morphology and clinical variables.

Purpose of the Study:

  • To introduce Graphical-Model-based Morphometric Analysis (GAMMA), a novel VBM method.
  • To automatically identify morphological abnormalities and complex probabilistic associations in MRI data.
  • To improve sensitivity, specificity, and computational efficiency in morphometric analysis.

Main Methods:

  • Developed GAMMA, a fully automatic, nonparametric VBM algorithm.
  • Employed a Bayesian network to model associations between voxels and clinical variables.

Related Experiment Videos

  • Utilized a contextual-clustering method based on a Markov random field and loopy belief propagation.

    • Main Results:

    • GAMMA demonstrates high sensitivity and specificity in identifying morphological abnormalities.
    • The method effectively captures complex, nonlinear associations between brain structure and clinical variables.
    • GAMMA outperforms previous Bayesian morphometry approaches in accuracy and speed.

    Conclusions:

    • GAMMA represents a significant advancement in automated VBM analysis.
    • The algorithm's ability to detect nonlinear associations enhances its utility for clinical research.
    • GAMMA offers a more sensitive, specific, and computationally efficient tool for neuroimaging studies.