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

Breast tissue density quantification via digitized mammograms.

P K Saha1, J K Udupa, E F Conant

  • 1Department of Radiology, University of Pennsylvania, Philadelphia 19104, USA.

IEEE Transactions on Medical Imaging
|August 22, 2001
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

Monitoring and Modeling Population Exposures to Air Pollutants from Oil and Gas Development: Part 1. Predictive, Source-Oriented Modeling and Measurements to Evaluate Community Exposures to Air Pollutants and Noise from Unconventional Oil and Gas Development.

Research report (Health Effects Institute)·2026
Same author

Association of the circulating lipid panel, PCPro, with clinical outcomes in metastatic hormone-sensitive prostate cancer: post hoc analysis of the ENZAMET phase III randomised trial (ANZUP 1304).

Annals of oncology : official journal of the European Society for Medical Oncology·2025
Same author

Anatomic attention regions via optimal anatomy modeling and recognition for DL-based image segmentation.

Proceedings of SPIE--the International Society for Optical Engineering·2024
Same author

Optimal computed tomography-based biomarkers for prediction of incisional hernia formation.

Hernia : the journal of hernias and abdominal wall surgery·2023
Same author

Environmental and Edaphic Factors that Influence Spring Dead Spot Epidemics.

Phytopathology·2023
Same author

Relationship of Serum Uric Acid Level with Acute Ischemic Stroke: A Single-Center, Hospital-Based Study from Bangladesh.

Mymensingh medical journal : MMJ·2022
Same journal

MUST: Multi-style virtual staining with incomplete pairs.

IEEE transactions on medical imaging·2026
Same journal

BrainCL: Transformer-Based Brain Network Contrastive Learning with Multi-Order Topology and Salience Masking.

IEEE transactions on medical imaging·2026
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
See all related articles

This study introduces an automated method for segmenting dense breast tissue in mammograms. This quantitative approach aids in assessing breast cancer risk and may improve screening strategies.

Area of Science:

  • Medical imaging
  • Radiology
  • Computer-aided diagnosis

Background:

  • Mammographic density is a known risk factor for breast cancer.
  • Objective, quantitative measures of mammographic density are needed for risk assessment and personalized screening.
  • Current automated methods for mammographic density segmentation are limited.

Purpose of the Study:

  • To develop and validate an automatic and reproducible method for segmenting dense breast tissue from fat in mammograms.
  • To compute and evaluate different mammographic density measures.
  • To compare the proposed method's accuracy against manual segmentation.

Main Methods:

  • Utilized scale-based fuzzy connectivity methods for image segmentation.
  • Segmented dense tissue regions from digitized mammograms.

Related Experiment Videos

  • Computed various mammographic density parameters from segmented regions.
  • Assessed robustness across cranio-caudal and medio-lateral-oblique views.
  • Validated accuracy by comparing with manual segmentation outlines.

    • Main Results:

    • An automatic and reproducible method for segmenting dense breast tissue was developed and validated.
    • Robustness of density measures across different mammographic views was studied.
    • The proposed method demonstrated accuracy comparable to manual outlining.
    • Mammographic density parameters incorporating original intensity values showed potential advantages over area-based measures.

    Conclusions:

    • The developed automated method provides an objective and reproducible way to quantify mammographic density.
    • This quantitative measure can potentially aid in risk stratification and inform personalized screening recommendations.
    • Further research into intensity-aware density parameters may enhance breast cancer risk assessment.