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

A hierarchical parametric algorithm for deformable multimodal image registration.

Pierre Hellier1, Christian Barillot

  • 1IRISA, INRIA-CNRS, Campus de Beaulieu, 35042 Rennes Cedex, France. phellier@irisa.fr

Computer Methods and Programs in Biomedicine
|June 24, 2004
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

Exploiting Latent Properties to Optimize Neural Codecs.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord.

Brain and behavior·2023
Same author

An Iterative Centroid Approach for Diffeomorphic Online Atlasing.

IEEE transactions on medical imaging·2022
Same author

Building memories on prior knowledge: behavioral and fMRI evidence of impairment in early Alzheimer's disease.

Neurobiology of aging·2021
Same author

Visual feedback improves movement illusions induced by tendon vibration after chronic stroke.

Journal of neuroengineering and rehabilitation·2021
Same author

Multiple sclerosis lesions segmentation from multiple experts: The MICCAI 2016 challenge dataset.

NeuroImage·2021
Same journal

SynTME: A tumor microenvironment-aware, pharmacology-inspired multi-stage framework for drug synergy prediction.

Computer methods and programs in biomedicine·2026
Same journal

MMFVS-Net: A triple-symmetric cross-attention network for multimodal optical image fusion and high-accuracy virtual staining of breast cancer tissues.

Computer methods and programs in biomedicine·2026
Same journal

A novel Milstein-stochastic epidemiologically-informed neural network for approaching epidemic dynamics: Application to Mpox disease.

Computer methods and programs in biomedicine·2026
Same journal

Accounting for approximation errors using surrogate-based parameter estimation of cardiac mechanics digital twins.

Computer methods and programs in biomedicine·2026
Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026
Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026
See all related articles

This study introduces a new method for fusing multimodal medical images, essential for combining complementary information. The technique effectively handles non-rigid transformations, improving image registration accuracy for better analysis.

Area of Science:

  • Medical imaging
  • Image analysis
  • Computer vision

Background:

  • Image fusion is crucial for integrating complementary data from different imaging modalities.
  • Accurate image registration is necessary for effective fusion, often requiring alignment of multimodal datasets.
  • While rigid transformations suffice for some cases, non-rigid transformations are vital for addressing geometrical distortions and motion artifacts.

Purpose of the Study:

  • To propose a generic, adaptable method for multimodal image fusion that accommodates non-rigid transformations.
  • To develop a robust algorithm capable of handling complex deformations in image registration.
  • To advance the application of image fusion in medical imaging analysis.

Main Methods:

  • Development of a generic algorithm designed for multimodal image fusion.

Related Experiment Videos

  • Implementation of non-rigid transformation techniques to correct for geometrical distortions.
  • Application and validation of the algorithm in the context of 3D medical imaging.

    • Main Results:

    • The proposed method successfully accounts for deformations in multimodal image fusion.
    • Demonstrated efficacy on both simulated and real 3D medical image datasets.
    • Achieved accurate superimposition of complementary information from different imaging modalities.

    Conclusions:

    • The developed generic method provides a robust solution for multimodal image fusion, particularly when non-rigid transformations are required.
    • This approach enhances the integration of complementary information in medical imaging, leading to improved diagnostic capabilities.
    • The algorithm's successful application on 3D medical data highlights its potential for clinical use.