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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
TitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsAndrew D. A. Maidment, Predrag R. Bakic, Sara Gavenonis
PublisherSpringer
Publication date2012
Pages181-188
Number of pages8
Volume7361 LNCS
ISBN (Print)978-3-642-31270-0
DOIs
StatePublished

Workshop

Workshop11th International Workshop on Breast Imaging
CountryUnited States
CityPhiladelphia
Period8/07/1211/07/12
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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