Characterizing breast phenotype with a novel measure of fibroglandular structure

TY - CHAP

T1 - Characterizing breast phenotype with a novel measure of fibroglandular structure

A1 - Hipwell,John H.

A1 - Griffin,Lewis D.

A1 - Whelehan,Patsy J.

A1 - Song,Wenlong

A1 - Zhang,Xiying

A1 - Lesniak,Jan M.

A1 - Vinnicombe,Sarah

A1 - Evans,Andy

A1 - Berg,Jonathan

A1 - Hawkes,David J.

AU - Hipwell,John H.

AU - Griffin,Lewis D.

AU - Whelehan,Patsy J.

AU - Song,Wenlong

AU - Zhang,Xiying

AU - Lesniak,Jan M.

AU - Vinnicombe,Sarah

AU - Evans,Andy

AU - Berg,Jonathan

AU - Hawkes,David J.

PB - Springer

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?partnerID=yv4JPVwI&eid=2-s2.0-84864832809&md5=a079c84ad4757cc392f64273a50368f0

U2 - 10.1007/978-3-642-31271-7_24

DO - 10.1007/978-3-642-31271-7_24

M1 - Other chapter contribution

SN - 978-3-642-31270-0

VL - 7361 LNCS

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Gavenonis,Sara

ED - Gavenonis,Sara

SP - 181

EP - 188

ER -