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Proceedings Paper

A novel phantom system facilitating better descriptors of density within mammographic images
Author(s): Yanpeng Li; Patrick C. Brennan; Carolyn Nickson; Mariusz W. Pietrzyk; Dana Al Mousa; Elaine Ryan
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Paper Abstract

High mammographic density is a risk factor for breast cancer. As it is impossible to measure actual weight or volume of fibroglandular tissue evident within a mammogram, it is hard to know the correlation between measured mammographic density and the actual fibroglandular tissue volume. The aim of this study is to develop a phantom that represents glandular tissue within an adipose tissue structure so that correlations between image feature descriptors and the synthesised glandular structure can be accurately quantified. In this phantom study, ten different weights of fine steel wool were put into gelatine to simulate breast structure. Image feature descriptors are investigated for both the whole phantom image and the simulated density. Descriptors included actual area and percentage area of density, mean pixel intensity for the whole image and dense area, standard deviation of mean intensity, and integrated pixel density which is the production of area and mean intensity. The results show high level correlation between steel-wool weight and percentage density measured on images (r = 0.8421), and the integrated pixel density of dense area (r = 0.8760). The correlation is significant for mean intensity standard deviation for the whole phantom (r = 0.8043). This phantom study may help identify more accurate descriptors of mammographic density, thus facilitating better assessments of fibroglandular tissue appearances.

Paper Details

Date Published: 28 March 2013
PDF: 7 pages
Proc. SPIE 8673, Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment, 86731S (28 March 2013); doi: 10.1117/12.2007760
Show Author Affiliations
Yanpeng Li, The Univ. of Sydney (Australia)
Patrick C. Brennan, The Univ. of Sydney (Australia)
Carolyn Nickson, The Univ. of Melbourne (Australia)
Mariusz W. Pietrzyk, The Univ. of Sydney (Australia)
Dana Al Mousa, The Univ. of Sydney (Australia)
Elaine Ryan, The Univ. of Sydney (Australia)

Published in SPIE Proceedings Vol. 8673:
Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Claudia R. Mello-Thoms, Editor(s)

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