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

MCMC curve sampling for image segmentation.

Ayres C Fan1, John W Fisher, William M Wells

  • 1Laboratory for Information and Decision Systems, MIT, Cambridge, MA, USA. fan@mit.edu

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|November 30, 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 authorSame 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 author

Sample-Specific Debiasing for Better Image-Text Models.

Proceedings of machine learning research·2026
Same author

Study of sex differences in the whole brain white matter using diffusion MRI tractography and suprathreshold fiber cluster statistics.

NeuroImage. Clinical·2026
Same author

Intraoperative Registration by Cross-Modal Inverse Neural Rendering.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2025
Same author

Two Projections Suffice for Cerebral Vascular Reconstruction.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2025
Same author

Patterns of Prior Induced Abortions and the Likelihood of Subsequent Natural Pregnancy Loss: Exploratory Application of Pregnancy Outcome Sequencing.

JMIR formative research·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

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 Markov chain Monte Carlo (MCMC) algorithm for generating curve samples from probability distributions. This method offers improved robustness and better characterization of complex data distributions.

Area of Science:

  • Computational geometry
  • Statistical modeling
  • Image analysis

Background:

  • Traditional curve evolution methods often rely on energy functionals.
  • Sampling from complex probability distributions on curve spaces is challenging.
  • Existing methods may struggle with local minima and multi-modal data.

Purpose of the Study:

  • To develop an algorithm for generating samples from probability distributions defined on the space of curves.
  • To leverage Markov chain Monte Carlo (MCMC) methods for curve sampling.
  • To demonstrate the advantages of sampling-based approaches over traditional methods.

Main Methods:

  • Interpreting curve evolution energy functionals as negative log probability distributions.
  • Employing a Markov chain Monte Carlo (MCMC) algorithm for sampling.

Related Experiment Videos

  • Defining a proposal distribution using smooth perturbations to the curve's normal vector.
  • Calculating transition probabilities to ensure posterior distribution sampling.

    • Main Results:

    • The proposed MCMC algorithm successfully generates samples from probability distributions on curve spaces.
    • Demonstrated robustness to local minima in energy landscapes.
    • Showcased improved characterization of multi-modal curve distributions.
    • Enabled access to estimation error measures and incorporation of curve constraints.

    Conclusions:

    • Sampling methods, particularly MCMC, provide a powerful alternative for analyzing curve data.
    • The developed algorithm offers enhanced flexibility and accuracy in curve modeling.
    • This approach facilitates a more comprehensive understanding of curve properties and uncertainties.