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

Study and performance evaluation of statistical methods in image processing.

Z Liang1, R Jaszczak, H Hart

  • 1Department of Radiology, Duke University Medical Center, Durham, NC 27710.

Computers in Biology and Medicine
|January 1, 1988
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

Altered fear processing in adolescents with a history of severe childhood maltreatment: an fMRI study.

Psychological medicine·2018
Same author

Grey matter volume and thickness abnormalities in young people with a history of childhood abuse.

Psychological medicine·2017
Same author

Probing lepton flavour violation via neutrinoless [Formula: see text] decays with the ATLAS detector.

The European physical journal. C, Particles and fields·2017
Same author

A new method to distinguish hadronically decaying boosted <i>Z</i> bosons from <i>W</i> bosons using the ATLAS detector.

The European physical journal. C, Particles and fields·2017
Same author

Search for supersymmetry at [Formula: see text] TeV in final states with jets and two same-sign leptons or three leptons with the ATLAS detector.

The European physical journal. C, Particles and fields·2017
Same author

Measurement of the differential cross-sections of prompt and non-prompt production of [Formula: see text] and [Formula: see text] in <i>pp</i> collisions at [Formula: see text] and 8 TeV with the ATLAS detector.

The European physical journal. C, Particles and fields·2017
Same journal

Rapid personalisation of cardiovascular models using invasively measured right ventricular pressure.

Computers in biology and medicine·2026
Same journal

Biologically inspired mechanisms for enhancing robustness in EEG signal modeling: Challenges, opportunities, and perspectives.

Computers in biology and medicine·2026
Same journal

Machine learning-based detection of missed inspiratory efforts using esophageal pressure during noisy pressure support ventilation.

Computers in biology and medicine·2026
Same journal

A computational model of chemically- and mechanically-induced thrombus formation in cerebral aneurysms.

Computers in biology and medicine·2026
Same journal

An improved catch fish optimization based deep learning model for Parkinson disease classification using EEG signal.

Computers in biology and medicine·2026
Same journal

Assessing the robustness of evaluation metrics for synthetic ECG signal quality.

Computers in biology and medicine·2026
See all related articles

This study compares entropy and Bayesian image processing algorithms. Bayesian methods offer more robust convergence with noisy data, outperforming entropy-based approaches in statistical image reconstruction.

Area of Science:

  • Statistical image processing
  • Medical imaging
  • Computational analysis

Background:

  • Image reconstruction is crucial in fields like radioisotope imaging.
  • Statistical methods, including entropy and Bayesian analysis, are employed for image processing.
  • Evaluating algorithm performance under noisy conditions is essential.

Purpose of the Study:

  • To formulate and compare iterative imaging algorithms based on entropy and Bayesian formalisms.
  • To quantitatively evaluate and compare the convergence performance of these algorithms.
  • To assess algorithm robustness against noise in imaging data.

Main Methods:

  • Formulation of iterative algorithms using steepest descent for entropy and expectation maximization for Bayesian analysis.

Related Experiment Videos

  • Quantitative evaluation using computer-generated ideal and experimental radioisotope phantom imaging data.
  • Comparison of convergence performance and noise sensitivity.

    • Main Results:

    • The entropy algorithm converges rapidly but is highly sensitive to noise due to ill-posed inverse problems.
    • The Bayesian algorithm demonstrates monotonic convergence, even with noisy data.
    • Bayesian analysis effectively incorporates prior information and accounts for data statistical fluctuations.

    Conclusions:

    • Bayesian image reconstruction offers superior robustness and performance with noisy data compared to entropy-based methods.
    • The ability to integrate prior knowledge and handle data statistics makes Bayesian approaches advantageous.
    • Entropy-based methods require careful consideration of noise and problem ill-posedness in practical applications.