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Breast Density Analysis Using an Automatic Density Segmentation Algorithm.

Arnau Oliver1, Meritxell Tortajada2,3, Xavier Lladó2

  • 1Department of Computer Architecture and Technology, University of Girona, 17071, Girona, Spain. aoliver@eia.udg.edu.

Journal of Digital Imaging
|February 28, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method for segmenting breast density in mammograms. The algorithm accurately measures breast density changes over time, aiding in breast cancer risk assessment.

Keywords:
Breast tissue densityComputer-assisted image interpretationLongitudinal studiesMammographySegmentation

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Area of Science:

  • Radiology
  • Medical Imaging Analysis
  • Biomedical Engineering

Background:

  • Breast density is a significant risk factor for breast cancer.
  • Accurate breast density assessment is crucial for risk stratification and early detection.

Purpose of the Study:

  • To develop and validate an automated algorithm for breast density segmentation in mammographic images.
  • To assess the algorithm's utility in studying longitudinal changes in breast density.

Main Methods:

  • A supervised pixel-based classification approach using textural and morphological features was employed.
  • The algorithm was trained and validated on a database of 130 patients with three screening examinations acquired 2 years apart.
  • Validation involved comparing automated segmentations with manual expert annotations.

Main Results:

  • High correlation coefficients (ρ=0.96 for left/right breasts, ρ=0.95 for CC/MLO views) were observed in transversal analyses, indicating robust performance.
  • Longitudinal analysis confirmed a general decrease in dense tissue percentage over time.
  • The rate of decrease in breast density was found to be dependent on the initial density levels.

Conclusions:

  • The automated breast density segmentation algorithm is feasible and accurate.
  • The developed method shows potential for application in longitudinal studies to monitor breast density evolution.
  • This tool can contribute to a better understanding of breast cancer risk factors and disease progression.