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Related Experiment Video

Updated: Jul 3, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Automated effect-specific mammographic pattern measures.

Jakob Raundahl1, Marco Loog, Paola Pettersen

  • 1Department of Computer Science (DIKU), University of Copenhagen, 2100 Copenhagen, Denmark. raundahl@gmail.com

IEEE Transactions on Medical Imaging
|August 2, 2008
PubMed
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This study introduces a machine learning approach to objectively measure breast tissue changes from mammograms, detecting age and hormone replacement therapy (HRT) effects where standard methods fail.

Area of Science:

  • Biomedical imaging
  • Machine learning
  • Radiology

Background:

  • Mammographic density is a key breast cancer risk factor.
  • Current methods for assessing breast tissue changes lack objectivity and sensitivity.
  • Hormone replacement therapy (HRT) is known to affect breast tissue density.

Purpose of the Study:

  • To develop a statistical machine learning framework for assessing effect-specific structural changes in breast tissue.
  • To create objective mammographic pattern measures that quantify biological effects like HRT.
  • To compare the proposed method's performance against standard density measures and interactive methods.

Main Methods:

  • Utilized a general statistical machine learning framework.
  • Developed objective mammographic pattern measures.

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Clinical Imaging of Microwave Mammography
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Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

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Last Updated: Jul 3, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Clinical Imaging of Microwave Mammography
05:28

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

  • Quantified age-related effects and effects of hormone replacement therapy (HRT).
  • Compared results with standard density measures and an interactive methodology.

    • Main Results:

    • The proposed method successfully quantified both age-related effects and HRT effects.
    • Age-related effects were significantly detected by the new method, unlike standard methodologies.
    • The separation of HRT subpopulations using the proposed approach was comparable to the best interactive methodology.

    Conclusions:

    • The developed machine learning approach provides objective and sensitive measures of breast tissue structural changes.
    • This method can identify age-related changes and effects of HRT more effectively than standard techniques.
    • The approach shows promise for improved mammographic analysis in clinical practice.