Breast Density Estimation in Mammograms Using Unsupervised Image Segmentation

Khaldoon Alhusari, Salam Dhou

2025

Abstract

Breast cancer is very common, and early detection through mammography is paramount. Breast density, a strong risk factor for breast cancer, can be estimated from mammograms. Current density estimation methods can be subjective, labor-intensive, and proprietary. This work proposes a framework for breast density estimation based on the unsupervised segmentation of mammograms. A state-of-the-art unsupervised image segmentation algorithm is adopted for the purpose of breast density segmentation. Mammographic percent density is estimated through a process of arithmetic division. The percentages are then discretized into qualitative assessments of density (“Fatty” and “Dense”) using a thresholding approach. Evaluation reveals robust segmentation at the pixel-level with silhouette scores averaging 0.95 and significant unsupervised labeling quality at the per-image level with silhouette scores averaging 0.61. The proposed framework is highly adaptable, generalizable, and non-subjective, and has the potential to be a beneficial support tool for radiologists.

Paper Citation

in Harvard Style

Alhusari K. and Dhou S. (2025). Breast Density Estimation in Mammograms Using Unsupervised Image Segmentation. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 691-698. DOI: 10.5220/0013297500003912

in Bibtex Style

@conference{visapp25,
author={Khaldoon Alhusari and Salam Dhou},
title={Breast Density Estimation in Mammograms Using Unsupervised Image Segmentation},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={691-698},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013297500003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Breast Density Estimation in Mammograms Using Unsupervised Image Segmentation
SN - 978-989-758-728-3
AU - Alhusari K.
AU - Dhou S.
PY - 2025
SP - 691
EP - 698
DO - 10.5220/0013297500003912
PB - SciTePress