Local breast density assessment using reacquired mammographic images

Eloy García; Oliver Diaz; Robert Martí; Yago Diez; Albert Gubern-Mérida; Melcior Sentís; Joan Martí; Arnau Oliver

doi:10.1016/j.ejrad.2017.05.033

Local breast density assessment using reacquired mammographic images

Eur J Radiol. 2017 Aug:93:121-127. doi: 10.1016/j.ejrad.2017.05.033. Epub 2017 May 30.

Authors

Eloy García¹, Oliver Diaz¹, Robert Martí¹, Yago Diez², Albert Gubern-Mérida³, Melcior Sentís⁴, Joan Martí¹, Arnau Oliver⁵

Affiliations

¹ Computer Vision and Robotics Institute, University of Girona, Spain.
² Tokuyama Laboratory GSIS, Tohoku University, Sendai, Japan.
³ Radboud University Medical Center, Nijmegen, The Netherlands.
⁴ UDIAT - Centre Diagnòstic, Corporació Parc Taulí, Sabadell, Spain.
⁵ Computer Vision and Robotics Institute, University of Girona, Spain. Electronic address: arnau.oliver@udg.edu.

PMID: 28668405
DOI: 10.1016/j.ejrad.2017.05.033

Abstract

Purpose: The aim of this paper is to evaluate the spatial glandular volumetric tissue distribution as well as the density measures provided by Volpara™ using a dataset composed of repeated pairs of mammograms, where each pair was acquired in a short time frame and in a slightly changed position of the breast.

Materials and methods: We conducted a retrospective analysis of 99 pairs of repeatedly acquired full-field digital mammograms from 99 different patients. The commercial software Volpara™ Density Maps (Volpara Solutions, Wellington, New Zealand) is used to estimate both the global and the local glandular tissue distribution in each image. The global measures provided by Volpara™, such as breast volume, volume of glandular tissue, and volumetric breast density are compared between the two acquisitions. The evaluation of the local glandular information is performed using histogram similarity metrics, such as intersection and correlation, and local measures, such as statistics from the difference image and local gradient correlation measures.

Results: Global measures showed a high correlation (breast volume R=0.99, volume of glandular tissue R=0.94, and volumetric breast density R=0.96) regardless the anode/filter material. Similarly, histogram intersection and correlation metric showed that, for each pair, the images share a high degree of information. Regarding the local distribution of glandular tissue, small changes in the angle of view do not yield significant differences in the glandular pattern, whilst changes in the breast thickness between both acquisition affect the spatial parenchymal distribution.

Conclusions: This study indicates that Volpara™ Density Maps is reliable in estimating the local glandular tissue distribution and can be used for its assessment and follow-up. Volpara™ Density Maps is robust to small variations of the acquisition angle and to the beam energy, although divergences arise due to different breast compression conditions.

Keywords: Breast cancer; Breast density; Image analysis; Mammography; Volpara™.

MeSH terms

Breast Density*
Female
Humans
Mammography / methods*
New Zealand
Retrospective Studies
Software