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

Modeling fiber type grouping by a binary Markov random field.

H W Venema1

  • 1Laboratory of Medical Physics and Informatics, University of Amsterdam, The Netherlands.

Muscle & Nerve
|June 1, 1992
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

The feasibility of colorectal cancer detection using dual-energy computed tomography with iodine mapping.

Clinical radiology·2013
Same author

Model-based measurements of the diameter of the internal carotid artery in CT angiography images.

Medical physics·2010
Same author

Intracranial CT angiography obtained from a cerebral CT perfusion examination.

Medical physics·2009
Same author

Radiation dose in CT colonography--trends in time and differences between daily practice and screening protocols.

European radiology·2008
Same author

Protrusion method for automated estimation of polyp size on CT colonography.

AJR. American journal of roentgenology·2008
Same author

Removal of bone in CT angiography by multiscale matched mask bone elimination.

Medical physics·2007
Same journal

Join AANEM.

Muscle & nerve·2026
Same journal

Targeted Muscle Reinnervation for Management and Prevention of Symptomatic Neuroma.

Muscle & nerve·2026
Same journal

AANEM News & Insights.

Muscle & nerve·2026
Same journal

Fat-Fraction Quantification Using Three-Point Dixon Technique in Duchenne Muscular Dystrophy and Its Correlation With Clinical Progression and Genotypic Characteristics: A Single Centre One-Year Prospective Study.

Muscle & nerve·2026
Same journal

Characterizing Combined Central and Peripheral Demyelination-Insights From a Multimodal Comparison With Chronic Inflammatory Demyelinating Polyneuropathy and Multiple Sclerosis.

Muscle & nerve·2026
Same journal

Electrical Modalities in the Rehabilitation of Peripheral Nerve Injuries: State of the Literature and Current Clinical Applications.

Muscle & nerve·2026
See all related articles

This study introduces a novel method using binary Markov random fields (BMRF) to quantify muscle fiber type grouping. The parameter beta effectively measures the degree of grouping, offering new insights into muscle histochemistry.

Area of Science:

  • Muscle Histochemistry
  • Biostatistics
  • Computational Biology

Background:

  • Quantifying muscle fiber type grouping is crucial for diagnosing neuromuscular disorders.
  • Current methods may lack precision in assessing the spatial distribution of fiber types.
  • A statistical modeling approach can offer a more objective quantification.

Purpose of the Study:

  • To present a new computational approach for quantifying muscle fiber type grouping.
  • To model the distribution of histochemical fiber types as a binary Markov random field (BMRF).
  • To estimate and interpret the parameters of the BMRF model, particularly the interaction parameter beta.

Main Methods:

  • Utilizing a first-order binary Markov random field (BMRF) model.
  • Developing methods for estimating the BMRF parameters (alpha and beta).

Related Experiment Videos

  • Applying the model to analyze 9 muscle biopsy samples.

    • Main Results:

    • The parameter beta, an interaction parameter, significantly quantifies the degree of fiber type grouping.
    • Estimation of beta was performed for 9 muscle biopsies.
    • The study discusses the interpretation of these quantitative results.

    Conclusions:

    • The BMRF model provides a robust framework for quantifying muscle fiber type grouping.
    • The parameter beta is a key metric for assessing the extent of fiber type clustering.
    • This approach enhances the objective analysis of muscle histochemical data.