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Publication Detail
Characterizing breast phenotype with a novel measure of fibroglandular structure
  • Publication Type:
    Conference
  • Authors:
    Hipwell JH, Griffin LD, Whelehan PJ, Song W, Zhang X, Lesniak JM, Vinnicombe S, Evans A, Berg J, Hawkes DJ
  • Publication date:
    15/08/2012
  • Pagination:
    181, 188
  • Published proceedings:
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volume:
    7361 LNCS
  • ISBN-13:
    9783642312700
  • Status:
    Published
  • Print ISSN:
    0302-9743
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 om , computed from this histogram. H om , shows good correlation between left and right breasts (r 2=0.76, N=478), and is independent of both mammographic breast area, a surrogate for breast size (r 2=0.07, N=974), and breast density, as estimated using Volpara TM software (r 2=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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