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Characterizing breast phenotype with a novel measure of fibroglandular structure

Characterizing breast phenotype with a novel measure of fibroglandular structure

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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

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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
Pages181-188
Number of pages8
ISBN (Electronic)9783642312717
ISBN (Print)9783642312700
DOIs
StatePublished - 2012

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
Internet address

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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