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Proceedings Paper

Mammographic density descriptors of novel phantom images: effect of clustered lumpy backgrounds
Author(s): Yanpeng Li; Patrick C. Brennan; Elaine Ryan
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Paper Abstract

Mammographic breast density (MBD) is a risk factor for breast cancer. Both qualitative and quantitative methods have been used to evaluate MBD. However as it is impossible to measure the actual weight or volume of fibroglandular tissue evident on a mammogram, therefore it is hard to know the true correlation between measured mammographic density and the fibroglandular tissue volume. A phantom system has been developed that represents glandular tissue within an adipose tissue structure. Although a previous study has found strong correlation between the synthesised glandular mass and several image descriptors, it is not known if the correlation is still present when a high level of background noise is introduced. The background noise is required to more realistically simulate clinical image appearance. The aim of this study is to investigate if the correlation between percentage density, integrated density, and standard deviation of mean grey value of the whole phantom and simulated glandular tissue mass is affected by background noise being added to the phantom images. For a set of one hundred phantom mammographic images, clustered lumpy backgrounds were synthesised and superimposed onto phantom images. The correlation between the synthesised glandular mass and the image descriptors were calculated. The results showed the correlation is strong and statistically significant for the above three descriptors with r is 0.7597, 0.8208, and 0.7167 respectively. This indicates these descriptors may be used to assess breast fibroglandular tissue content of the breast using mammographic images.

Paper Details

Date Published: 11 March 2014
PDF: 6 pages
Proc. SPIE 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 90370H (11 March 2014); doi: 10.1117/12.2043345
Show Author Affiliations
Yanpeng Li, The Univ. of Sydney (Australia)
Patrick C. Brennan, The Univ. of Sydney (Australia)
Elaine Ryan, The Univ. of Sydney (Australia)


Published in SPIE Proceedings Vol. 9037:
Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment
Claudia R. Mello-Thoms; Matthew A. Kupinski, Editor(s)

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