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

A representation for mammographic image processing

R Highnam1, M Brady, B Shepstone

  • 1Department of Engineering Science, Oxford University, UK. rph@robots.ox.ac.uk

Medical Image Analysis
|March 1, 1996
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

Is MRSF-TDDFT suitable for cyclobutanone dynamics? The role of higher energy states in surface hopping simulations.

The Journal of chemical physics·2025
Same author

Renal function and iodinated contrast media: a National Institute for Health and Care Excellence update.

Clinical radiology·2025
Same author

Experiences of Joint Dental Training Programme in East of England.

British dental journal·2024
Same author

The Boomerang Study - Increased Hospital Re-Admission via the Emergency Department.

Irish medical journal·2023
Same author

Evolving relationship of Nares Strait ice arches on sea ice along the Strait and the North Water, the Arctic's most productive polynya.

Scientific reports·2023
Same author

Bayesian networks and imaging-derived phenotypes highlight the role of fat deposition in COVID-19 hospitalisation risk.

Frontiers in bioinformatics·2023
Same journal

Co-assistant networks by pathology foundation model and convolutional neural network for gigapixel whole slide image analysis.

Medical image analysis·2026
Same journal

MBAS2024: A large-scale benchmark for multi-class bi-atrial segmentation in multi-center contrast-enhanced MRIs.

Medical image analysis·2026
Same journal

Respiratory motion augmentation for personalized super-resolution (RMApSR) of 3D cine MR images in MRI-guided radiotherapy.

Medical image analysis·2026
Same journal

Biom3d, a modular framework to host and develop 3D segmentation methods.

Medical image analysis·2026
Same journal

Embracing intra-class heterogeneity for semi-supervised medical image segmentation: From diversity to precision.

Medical image analysis·2026
Same journal

Real-time patient-specific microwave ablation zone prediction via a unified bioheat solver and MRI-informed perturbation learning.

Medical image analysis·2026
See all related articles

A new physics-based method quantifies breast tissue using the h(int) representation, improving mammogram analysis. This approach enhances image quality and anatomical understanding for more robust diagnostics.

Area of Science:

  • Medical Imaging
  • Biophysics
  • Radiology

Background:

  • Standard mammographic image analysis relies on general-purpose algorithms, posing potential risks.
  • A physics-model-based approach offers an alternative for calibrating the mammographic imaging process.
  • Quantitative measures of breast tissue are crucial for accurate analysis.

Purpose of the Study:

  • To develop and describe a physics-model-based approach for mammographic image analysis.
  • To introduce the h(int) representation as a quantitative measure of breast tissue.
  • To demonstrate the utility of the h(int) representation in various mammographic image processing tasks.

Main Methods:

  • Developed a physics-model-based calibration for mammography.
  • Introduced the h(int) representation, quantifying non-fat tissue thickness.

Related Experiment Videos

  • Estimated breast thickness from images and analyzed its sensitivity on h(int) computation.
  • Simulated projective X-ray examinations and anatomical structures.
  • Compared h(int) representation with conventional methods regarding invariance.

    • Main Results:

    • The h(int) representation provides a quantitative measure of breast tissue, acting as an anatomical surface.
    • This representation enables image enhancement and normalization by removing imaging condition variations.
    • Features derived from h(int) are robust to breast compression and composition variations.
    • Breast thickness estimation from images is feasible and its sensitivity analyzed.
    • Comparison shows the h(int) representation is more invariant to imaging conditions and surrounding tissue.

    Conclusions:

    • The h(int) representation offers a robust basis for mammographic image analysis.
    • This physics-model-based approach significantly enhances image analysis robustness and diagnostic potential.
    • Considering the imaging process and utilizing the h(int) representation leads to more reliable image analysis.