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Updated: Nov 30, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Density map and fuzzy classification for breast density by using BI-RADS.

I Valencia-Hernandez1, H Peregrina-Barreto1, C A Reyes-Garcia1

  • 1Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, Santa Maria Tonantzintla, Puebla 72840, México.

Computer Methods and Programs in Biomedicine
|November 16, 2020
PubMed
Summary

This study introduces a novel breast density map using texture analysis to detail internal breast tissue composition. This tool aids physicians in monitoring breast density changes and classifying them according to BI-RADS categories.

Keywords:
BI-RADSBreast density classificationDensity map

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Area of Science:

  • Radiology and Medical Imaging
  • Biomedical Engineering
  • Computational Pathology

Background:

  • Mammographic density (MD) reflects breast tissue composition (stromal, epithelial, adipose).
  • High breast density is a critical risk factor and diagnostic challenge in breast cancer detection.
  • Existing methods for calculating general breast density lack detailed internal tissue analysis.

Purpose of the Study:

  • To develop a breast density map using texture analysis for detailed internal breast tissue composition and distribution.
  • To enable quantification and classification of breast densities aligned with BI-RADS categories.
  • To provide a tool for monitoring temporal changes in breast density.

Main Methods:

  • Texture analysis applied to mammograms to identify and map internal breast tissue composition.
  • Diffuse division technique to differentiate various density levels within the breast.
  • Fuzzy classification for quantifying membership in BI-RADS breast density classes.

Main Results:

  • The proposed methodology generated a breast density map identifying internal tissue composition and distribution.
  • The system achieved high accuracy in classifying breast densities: 84.2% on the BCDR database and 81.3% on the InBreast database.
  • Demonstrated consistency in results across different mammogram databases.

Conclusions:

  • The generated breast density map offers a detailed view of breast tissue composition beyond general density measurements.
  • This tool can assist specialists in monitoring breast density changes over time and improve breast cancer risk assessment.
  • Fuzzy classification provides a quantitative measure of BI-RADS density categories, enhancing diagnostic support.