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Characterizing breast phenotype with a novel measure of fibroglandular structure

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Characterizing breast phenotype with a novel measure of fibroglandular structure. / Hipwell, John H.; Griffin, Lewis D.; Whelehan, Patsy J.; Song, Wenlong; Zhang, Xiying; Lesniak, Jan M.; Vinnicombe, Sarah; Evans, Andy; Berg, Jonathan; Hawkes, David J.

Breast Imaging: 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012. Proceedings. ed. / Andrew D. A. Maidment; Predrag R. Bakic; Sara Gavenonis. Berlin : Springer , 2012. p. 181-188 (Lecture notes in computer science; Vol. 7361).

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

Harvard

Hipwell, JH, Griffin, LD, Whelehan, PJ, Song, W, Zhang, X, Lesniak, JM, Vinnicombe, S, Evans, A, Berg, J & Hawkes, DJ 2012, 'Characterizing breast phenotype with a novel measure of fibroglandular structure'. in ADA Maidment, PR Bakic & S Gavenonis (eds), Breast Imaging: 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012. Proceedings. Lecture notes in computer science, vol. 7361, Springer , Berlin, pp. 181-188, 11th International Workshop on Breast Imaging, Philadelphia, United States, 8-11 July., 10.1007/978-3-642-31271-7_24

APA

Hipwell, J. H., Griffin, L. D., Whelehan, P. J., Song, W., Zhang, X., Lesniak, J. M., ... Hawkes, D. J. (2012). Characterizing breast phenotype with a novel measure of fibroglandular structure. In A. D. A. Maidment, P. R. Bakic, & S. Gavenonis (Eds.), Breast Imaging: 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012. Proceedings. (pp. 181-188). (Lecture notes in computer science; Vol. 7361). Berlin: Springer . 10.1007/978-3-642-31271-7_24

Vancouver

Hipwell JH, Griffin LD, Whelehan PJ, Song W, Zhang X, Lesniak JM et al. Characterizing breast phenotype with a novel measure of fibroglandular structure. In Maidment ADA, Bakic PR, Gavenonis S, editors, Breast Imaging: 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012. Proceedings. Berlin: Springer . 2012. p. 181-188. (Lecture notes in computer science). Available from: 10.1007/978-3-642-31271-7_24

Author

Hipwell, John H.; Griffin, Lewis D.; Whelehan, Patsy J.; Song, Wenlong; Zhang, Xiying; Lesniak, Jan M.; Vinnicombe, Sarah; Evans, Andy; Berg, Jonathan; Hawkes, David J. / Characterizing breast phenotype with a novel measure of fibroglandular structure.

Breast Imaging: 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012. Proceedings. ed. / Andrew D. A. Maidment; Predrag R. Bakic; Sara Gavenonis. Berlin : Springer , 2012. p. 181-188 (Lecture notes in computer science; Vol. 7361).

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

Bibtex - Download

@inbook{69f3b40ec98d4534bf617544e9fe3dde,
title = "Characterizing breast phenotype with a novel measure of fibroglandular structure",
publisher = "Springer",
author = "Hipwell, {John H.} and Griffin, {Lewis D.} and Whelehan, {Patsy J.} and Wenlong Song and Xiying Zhang and Lesniak, {Jan M.} and Sarah Vinnicombe and Andy Evans and Jonathan Berg and Hawkes, {David J.}",
year = "2012",
doi = "10.1007/978-3-642-31271-7_24",
editor = "Maidment, {Andrew D. A.} and Bakic, {Predrag R.} and Sara Gavenonis",
isbn = "9783642312700",
series = "Lecture notes in computer science",
pages = "181-188",
booktitle = "Breast Imaging",

}

RIS (suitable for import to EndNote) - Download

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

CY - Berlin

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?scp=84864832809&partnerID=8YFLogxK

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

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

M3 - Conference contribution

SN - 9783642312700

BT - Breast Imaging

T2 - Breast Imaging

A2 - Gavenonis,Sara

ED - Gavenonis,Sara

T3 - Lecture notes in computer science

T3 - en_GB

SP - 181

EP - 188

ER -

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