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

3D dental image registration using exhaustive deformable models: a comparative study.

Maria-Pavlina Kalla1, Theodore L Economopoulos1, George K Matsopoulos1

  • 1School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.

Dento Maxillo Facial Radiology
|April 14, 2017
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

EEG Connectivity Signatures in Active vs. Passive Mental Fatigue Settings.

IEEE journal of biomedical and health informatics·2026
Same author

PANTHER Challenge Report: Cross-Domain Pancreatic Tumor Segmentation in Magnetic Resonance Imaging.

Medical image analysis·2026
Same author

Beyond target lesions: Prognostic value of longitudinal AI-derived whole-body [<sup>1</sup>⁸F]FDG PET/CT metrics in metastatic melanoma.

European journal of nuclear medicine and molecular imaging·2026
Same author

MRIgRT real-time target tracking: TrackRAD2025 challenge report.

Medical image analysis·2026
Same author

Saccadic eye movements based classification of patients with obsessive-compulsive disorder, patients with schizophrenia and healthy controls using artificial neural networks.

Cognitive neurodynamics·2026
Same author

Trochlear Nerve Palsy: A Systematic Review of Etiologies and Diagnostic Insights.

Diagnostics (Basel, Switzerland)·2025

The level-set motion method excels at aligning Cone Beam Computed Tomography (CBCT) volumes, outperforming other deformable models for accurate dental image registration. This technique is crucial for assessing changes in pathomorphic conditions.

Area of Science:

  • Medical Imaging
  • Computational Anatomy
  • Dental Radiology

Background:

  • Image registration is vital in dentistry for tracking pathomorphic changes.
  • Rigid body registration often proves insufficient for complex dental datasets.
  • Deformable models are necessary for accurate alignment of challenging imaging data.

Purpose of the Study:

  • To compare the performance of four deformable models for CBCT volume registration.
  • To evaluate the efficacy of different registration techniques in dental applications.
  • To identify the optimal deformable model for aligning CBCT data.

Main Methods:

  • Implemented a two-stage registration scheme: rigid pre-alignment followed by elastic registration.
  • Compared the original Demons, symmetric forces Demons, diffeomorphic Demons, and level-set motion algorithms.
Keywords:
Cone-beam computed tomographyDemons algorithmdiffeomorphic Demonselastic deformation modelsimage registrationlevel-set motionsymmetric forces Demons

Related Experiment Videos

  • Applied the framework to 40 pairs of CBCT volumes with varying initial differences.

    • Main Results:

    • The level-set motion method demonstrated superior performance across tested scenarios.
    • All methods were evaluated using both qualitative and quantitative assessments.
    • The framework successfully aligned CBCT volumes with known and unknown initial differences.

    Conclusions:

    • Level-set motion is the most effective deformable model for CBCT image registration in dental applications.
    • This method accurately aligns data with both unknown initial differences and known elastic deviations.
    • The findings support the use of level-set motion for precise assessment of dental pathomorphology.