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

Multi-modal image registration using the generalized survival exponential entropy.

Shu Liao1, 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. liaoshu@cse.ust.hk

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|March 16, 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

Pulmonary embolism associated with CRBN-targeting immunomodulatory drugs: a FAERS-based pharmacovigilance and mechanistic analysis.

Journal of thrombosis and thrombolysis·2026
Same author

Real-world unified denoising for multi-organ fast MRI: a large-scale prospective validation.

NPJ digital medicine·2026
Same author

Finding Significant Hits in Networks: a network-based tool for analyzing gene-level P-values to identify significant genes missed by standard methods.

Briefings in bioinformatics·2026
Same author

Whole blood transcriptional signatures of age and survival identified in long life family and integrative longevity omics studies.

GeroScience·2026
Same author

Combining Motifs, CRE Activity, And Gene Expression Data Using ML Greatly Improves the Accuracy of Tissue-Specific TF Network Maps.

bioRxiv : the preprint server for biology·2025
Same author

Whole blood transcriptional signatures of age and survival identified in Long Life Family and Integrative Longevity Omics Studies.

bioRxiv : the preprint server for biology·2025
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 presents a new generalized survival exponential entropy mutual information (GSEE-MI) for robust multimodal image registration. The GSEE-MI method reduces interpolation artifacts, showing comparable accuracy to traditional methods.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Information Theory

Background:

  • Multimodal image registration aligns images from different modalities (e.g., MRI, CT).
  • Traditional mutual information (MI) methods can be sensitive to interpolation artifacts.
  • Accurate registration is crucial for diagnosis and treatment planning.

Purpose of the Study:

  • To introduce a novel similarity measure, generalized survival exponential entropy mutual information (GSEE-MI), for multimodal image registration.
  • To evaluate the robustness and accuracy of the GSEE-MI measure compared to conventional MI.

Main Methods:

  • Developed a new similarity measure based on generalized survival exponential entropy (GSEE) and mutual information.
  • Estimated GSEE from the cumulative distribution function to mitigate interpolation artifacts.

Related Experiment Videos

  • Validated the GSEE-MI method on four real-world MR-CT datasets.

    • Main Results:

    • The GSEE-MI-based method demonstrated increased robustness compared to conventional MI.
    • Reduced interpolation artifacts were observed due to GSEE estimation from the cumulative distribution function.
    • The accuracy of the GSEE-MI method was found to be comparable to the conventional MI method.

    Conclusions:

    • The proposed GSEE-MI measure offers a robust alternative for multimodal image registration.
    • Utilizing the cumulative distribution function for entropy estimation effectively reduces interpolation artifacts.
    • GSEE-MI provides a promising approach for improving the reliability of image registration in medical applications.