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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.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Andrew D. A. Maidment; Predrag R. Bakic; Sara Gavenonis. Vol. 7361 LNCS Springer, 2012. p. 181-188.

Research output: Chapter in Book/Report/Conference proceedingOther chapter 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), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7361 LNCS, Springer, pp. 181-188, 11th International Workshop on Breast Imaging, Philadelphia, United States, 8-11 July.

APA

Hipwell, J. H., Griffin, L. D., Whelehan, P. J., Song, W., Zhang, X., Lesniak, J. M., Vinnicombe, S., Evans, A., Berg, J., & Hawkes, D. J. (2012). Characterizing breast phenotype with a novel measure of fibroglandular structure. In Maidment, A. D. A., Bakic, P. R., & Gavenonis, S. (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). (pp. 181-188). Springer. doi: 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, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer. 2012. p. 181-188.

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.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Andrew D. A. Maidment; Predrag R. Bakic; Sara Gavenonis. Vol. 7361 LNCS Springer, 2012. p. 181-188.

Research output: Chapter in Book/Report/Conference proceedingOther chapter 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",
editor = "Maidment, {Andrew D. A.} and Bakic, {Predrag R.} and Sara Gavenonis",
volume = "7361 LNCS",
isbn = "978-3-642-31270-0",
pages = "181-188",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

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

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 -

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