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

Polygonal and polyhedral contour reconstruction in computed tomography.

Charles Soussen1, Ali Mohammad-Djafari

  • 1Laboratoire des Signaux et Systèmes, Centre National de la Recherche Scientifique, Supélec, Gif-sur-Yvette, France. charles.soussen@limsi.fr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 16, 2004
PubMed
Summary

This study reconstructs 3-D polyhedral shapes from X-ray tomography data without voxel approximation. The novel method accurately estimates surfaces, improving image reconstruction quality and speed.

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

Physics-Informed Neural Networks with Unknown Partial Differential Equations: An Application in Multivariate Time Series.

Entropy (Basel, Switzerland)·2025
Same author

Variational Bayesian Approximation (VBA): Implementation and Comparison of Different Optimization Algorithms.

Entropy (Basel, Switzerland)·2024
Same author

Dynamical system identification, model selection, and model uncertainty quantification by Bayesian inference.

Chaos (Woodbury, N.Y.)·2024
Same author

An interpretable machine learning approach to study the relationship beetwen retrognathia and skull anatomy.

Scientific reports·2023
Same author

Regularization, Bayesian Inference, and Machine Learning Methods for Inverse Problems.

Entropy (Basel, Switzerland)·2021
Same author

Bayesian 3D X-ray Computed Tomography with a Hierarchical Prior Model for Sparsity in Haar Transform Domain.

Entropy (Basel, Switzerland)·2020

Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Geometry

Background:

  • X-ray tomography is crucial for 3-D imaging.
  • Reconstructing precise shapes from tomographic data remains challenging.
  • Existing methods often rely on voxel approximations, limiting accuracy.

Purpose of the Study:

  • To develop a novel method for three-dimensional (3-D) reconstruction of binary images from X-ray tomographic data.
  • To perform direct polyhedral surface estimation for compact, uniform polyhedra.
  • To avoid voxel approximation in the reconstruction process.

Main Methods:

  • Formulating the problem as a nonlinear inverse problem within a Bayesian framework.
  • Estimating vertices directly without voxel approximation.

Related Experiment Videos

  • Constructing and optimizing a regularized criterion for surface smoothness.
  • Developing deterministic local algorithms using exact line projections and their derivatives.

    • Main Results:

    • Successfully reconstructed 2-D polygonal objects and extended to 3-D polyhedral reconstruction.
    • Demonstrated high-quality reconstruction with efficient computation times.
    • Validated the performance of the proposed polygonal and polyhedral reconstruction algorithms through simulations.

    Conclusions:

    • The proposed method offers an accurate and efficient approach for 3-D polyhedral reconstruction from tomographic data.
    • Direct surface estimation without voxel approximation enhances reconstruction fidelity.
    • The algorithms show significant potential for applications in scientific imaging and analysis.