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International Journal of Breast Cancer
Volume 2015 (2015), Article ID 276217, 31 pages
Review Article

A Review on Automatic Mammographic Density and Parenchymal Segmentation

1Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK
2Department of Radiology, Norfolk & Norwich University Hospital, Norwich NR4 7UY, UK
3Department of Architecture and Computer Technology, University of Girona, 17071 Girona, Spain

Received 13 January 2015; Revised 21 April 2015; Accepted 17 May 2015

Academic Editor: Mireille Broeders

Copyright © 2015 Wenda He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models.