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

Non-rigid image registration using graph-cuts.

Tommy W H Tang1, Albert C S Chung

  • 1Lo Kwee-Seong Medical Image Analysis Laboratory, Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong. cstommy@cse.ust.hk

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|December 7, 2007
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

InstantGroup: Instant Template Generation for Scalable Group of Brain MRI Registration.

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

Depth-Aware Networks for Multi-Organ Lesion Detection in Chest CT Scans.

Bioengineering (Basel, Switzerland)·2024
Same author

EfficientQ: An efficient and accurate post-training neural network quantization method for medical image segmentation.

Medical image analysis·2024
Same author

Retinal Vessel Segmentation by a Transformer-U-Net Hybrid Model With Dual-Path Decoder.

IEEE journal of biomedical and health informatics·2024
Same author

A Dual Coordinate System Vertebra Landmark Detection Network with Sparse-to-Dense Vertebral Line Interpolation.

Bioengineering (Basel, Switzerland)·2024
Same author

Unsupervised Domain Adaptation for Medical Image Segmentation Using Transformer With Meta Attention.

IEEE transactions on medical imaging·2023
Same journal

LiftReg: Limited Angle 2D/3D Deformable Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Inverse Consistency by Construction for Multistep Deep Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Can Crowdsourced Annotations Improve AI-based Congestion Scoring For Bedside Lung Ultrasound?

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Equivariant Filters for Efficient Tracking in 3D Imaging.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Lobar Lung Density Embeddings with a Transformer encoder (LobTe) to predict emphysema progression in COPD.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

uniGradICON: A Foundation Model for Medical Image Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
See all related articles

This study introduces a novel graph-cuts approach for non-rigid image registration, treating it as a discrete labeling problem. The method achieves robust and accurate 2D and 3D image registration, outperforming existing techniques.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Computational Anatomy

Background:

  • Non-rigid image registration is a complex problem with high degrees of freedom and smoothness requirements.
  • The graph-cuts method is a powerful optimization tool successfully applied in image segmentation and stereo matching.
  • Exploring graph-cuts for non-rigid registration offers potential for robust optimization.

Purpose of the Study:

  • To formulate non-rigid image registration as a discrete labeling problem solvable with graph-cuts.
  • To investigate the effectiveness of graph-cuts optimization for non-rigid image registration.
  • To evaluate the accuracy and robustness of the proposed method against state-of-the-art approaches.

Main Methods:

  • Formulated non-rigid image registration as a discrete labeling problem.

Related Experiment Videos

  • Assigned displacement labels (vectors) to source image pixels for correspondence in the floating image.
  • Employed a first-derivative-based smoothness constraint and optimized using graph-cuts via alpha-expansions.

    • Main Results:

    • The proposed graph-cuts method demonstrated robust performance in challenging 2D and 3D non-rigid registration scenarios.
    • Achieved higher registration accuracy compared to two state-of-the-art methods.
    • Validated through comparative analysis of 2D and 3D registration outcomes.

    Conclusions:

    • Graph-cuts provide an effective optimization strategy for non-rigid image registration.
    • The discrete labeling approach with smoothness constraints enhances registration accuracy and robustness.
    • This method shows significant promise for advanced medical image analysis and other applications.