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Characterizing breast phenotype with a novel measure of fibroglandular structure

Characterizing breast phenotype with a novel measure of fibroglandular structure

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Authors

  • John H. Hipwell
  • Lewis D. Griffin
  • Patsy J. Whelehan
  • Wenlong Song
  • Xiying Zhang
  • Jan M. Lesniak
  • Sarah Vinnicombe
  • Andy Evans
  • Jonathan Berg
  • David J. Hawkes

Research units

Info

Original languageEnglish
Title of host publicationBreast Imaging
Subtitle of host publication11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012. Proceedings
EditorsAndrew D. A. Maidment, Predrag R. Bakic, Sara Gavenonis
Place of publicationBerlin
PublisherSpringer
Publication date2012
Pages181-188
Number of pages8
ISBN (Electronic)9783642312717
ISBN (Print)9783642312700
DOIs
StatePublished

Publication series

NameLecture notes in computer science
PublisherSpringer
Volume7361

Workshop

Workshop11th International Workshop on Breast Imaging
Abbreviated titleIWDM 2012
CountryUnited States
CityPhiladelphia
Period8/07/1211/07/12
Other
Internet addresshttp://www.iwdm2012.org/tiki-index.php

Abstract

Understanding, and accurately being able to predict, breast cancer risk would greatly enhance the early detection, and hence treatment, of the disease. In this paper we describe a new metric for mammographic structure, "orientated mammographic entropy", via a comprehensive classification of image pixels into one of seven basic image feature (BIF) classes. These classes are flat (zero order), slope-like (first order), and maximum, minimum, light-lines, dark-lines and saddles (second order). By computing a reference breast orientation with respect to breast shape and nipple location, these classes are further subdivided into 23 orientated BIF classes. For a given mammogram a histogram is constructed from the proportion of pixels in each of the 23 classes, and the orientated mammographic entropy, H , computed from this histogram. H , shows good correlation between left and right breasts (r =0.76, N=478), and is independent of both mammographic breast area, a surrogate for breast size (r =0.07, N=974), and breast density, as estimated using Volpara software (r =0.11, N=385). We illustrate this metric by examining its relationship to familial breast cancer risk, for 118 subjects, using the BOADICEA genetic susceptibility to breast and ovarian cancer model. © 2012 Springer-Verlag Berlin Heidelberg.

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