Breast Density Classification using Local Ternary Patterns in Mammograms

Andrik Rampun; Philip Morrow; Bryan Scotney; John Winder

doi:10.1007/978-3-319-59876-5

Breast Density Classification using Local Ternary Patterns in Mammograms

Andrik Rampun, Philip Morrow, Bryan Scotney, John Winder

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Citations (Scopus)

Abstract

This paper presents a method for breast density classifica- tion. Local ternary pattern operators are employed to model the ap- pearance of the fibroglandular disk region instead of the whole breast region as the majority of current studies have done. The Support Vector Machine classifier is used to perform the classification and a stratified ten-fold cross-validation scheme is employed to evaluate the performance of the method. The proposed method achieved 82.33% accuracy which is comparable with some of the best methods in the literature based on the same dataset and evaluation scheme.

Original language	English
Title of host publication	Unknown Host Publication
Publisher	Springer
Pages	463-470
Number of pages	8
Volume	10317
ISBN (Print)	978-3-319-59876-5
DOIs	https://doi.org/10.1007/978-3-319-59876-5
Publication status	Published online - 2 Jun 2017
Event	Image Analysis and Recognition 14th International Conference, ICIAR 2017, Montreal, QC, Canada, July 5–7, 2017 - Montreal, Canada Duration: 2 Jun 2017 → …

Conference

Conference	Image Analysis and Recognition 14th International Conference, ICIAR 2017, Montreal, QC, Canada, July 5–7, 2017
Period	2/06/17 → …

Keywords

Mammography
breast density
local ternary patterns
classification

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-319-59876-5

Cite this

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title = "Breast Density Classification using Local Ternary Patterns in Mammograms",

abstract = "This paper presents a method for breast density classifica- tion. Local ternary pattern operators are employed to model the ap- pearance of the fibroglandular disk region instead of the whole breast region as the majority of current studies have done. The Support Vector Machine classifier is used to perform the classification and a stratified ten-fold cross-validation scheme is employed to evaluate the performance of the method. The proposed method achieved 82.33% accuracy which is comparable with some of the best methods in the literature based on the same dataset and evaluation scheme.",

keywords = "Mammography, breast density, local ternary patterns, classification",

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AU - Rampun, Andrik

AU - Morrow, Philip

AU - Scotney, Bryan

AU - Winder, John

PY - 2017/6/2

Y1 - 2017/6/2

N2 - This paper presents a method for breast density classifica- tion. Local ternary pattern operators are employed to model the ap- pearance of the fibroglandular disk region instead of the whole breast region as the majority of current studies have done. The Support Vector Machine classifier is used to perform the classification and a stratified ten-fold cross-validation scheme is employed to evaluate the performance of the method. The proposed method achieved 82.33% accuracy which is comparable with some of the best methods in the literature based on the same dataset and evaluation scheme.

AB - This paper presents a method for breast density classifica- tion. Local ternary pattern operators are employed to model the ap- pearance of the fibroglandular disk region instead of the whole breast region as the majority of current studies have done. The Support Vector Machine classifier is used to perform the classification and a stratified ten-fold cross-validation scheme is employed to evaluate the performance of the method. The proposed method achieved 82.33% accuracy which is comparable with some of the best methods in the literature based on the same dataset and evaluation scheme.

KW - Mammography

KW - breast density

KW - local ternary patterns

KW - classification

U2 - 10.1007/978-3-319-59876-5

DO - 10.1007/978-3-319-59876-5

M3 - Conference contribution

SN - 978-3-319-59876-5

VL - 10317

SP - 463

EP - 470

BT - Unknown Host Publication

PB - Springer

T2 - Image Analysis and Recognition 14th International Conference, ICIAR 2017, Montreal, QC, Canada, July 5–7, 2017

Y2 - 2 June 2017

ER -

Breast Density Classification using Local Ternary Patterns in Mammograms

Abstract

Conference

Keywords

UN SDGs

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