A Quantitative Study of Local Ternary Patterns for Risk Assessment in Mammography

Andrik Rampun; PJ Morrow; BW Scotney; RJ Winder

doi:10.1007/978-3-319-59397-5_31

A Quantitative Study of Local Ternary Patterns for Risk Assessment in Mammography

Andrik Rampun, PJ Morrow, BW Scotney, RJ Winder

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

4 Citations (Scopus)

Abstract

This paper presents a preliminary quantitative study for breast cancer risk assessment in mammography using mathematical operators called Local Ternary Patterns. The study covers three different mapping patterns namely uniform (‘u2’), nonuniform (‘ri’) and a combination of uniform and nonuniform (‘riu2’). These patterns are used as texture features to model the appearance of breast density within the fibroglandular disk area. Subsequently, the Support Vector Machine is employed as a classification approach and initial results suggest that the mapping pattern ‘riu2’ outperforms the others.

Original language	English
Title of host publication	Unknown Host Publication
Publisher	Springer
Pages	283-286
Number of pages	4
Volume	71
ISBN (Print)	978-3-319-59396-8
DOIs	https://doi.org/10.1007/978-3-319-59397-5_31
Publication status	Published online - 21 May 2017
Event	International Conference on Innovation in Medicine and Healthcare 2017 - Vilamoura, Portugal Duration: 21 May 2017 → …

Conference

Conference	International Conference on Innovation in Medicine and Healthcare 2017
Period	21/05/17 → …

Keywords

Computer Aided Diagnosis
Local Ternary Patterns
Breast Cancer and Mammography

UN SDGs

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

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10.1007/978-3-319-59397-5_31

Cite this

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title = "A Quantitative Study of Local Ternary Patterns for Risk Assessment in Mammography",

abstract = "This paper presents a preliminary quantitative study for breast cancer risk assessment in mammography using mathematical operators called Local Ternary Patterns. The study covers three different mapping patterns namely uniform ({\textquoteleft}u2{\textquoteright}), nonuniform ({\textquoteleft}ri{\textquoteright}) and a combination of uniform and nonuniform ({\textquoteleft}riu2{\textquoteright}). These patterns are used as texture features to model the appearance of breast density within the fibroglandular disk area. Subsequently, the Support Vector Machine is employed as a classification approach and initial results suggest that the mapping pattern {\textquoteleft}riu2{\textquoteright} outperforms the others.",

keywords = "Computer Aided Diagnosis, Local Ternary Patterns, Breast Cancer and Mammography",

author = "Andrik Rampun and PJ Morrow and BW Scotney and RJ Winder",

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doi = "10.1007/978-3-319-59397-5_31",

language = "English",

isbn = "978-3-319-59396-8",

volume = "71",

pages = "283--286",

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note = "International Conference on Innovation in Medicine and Healthcare 2017 ; Conference date: 21-05-2017",

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TY - GEN

T1 - A Quantitative Study of Local Ternary Patterns for Risk Assessment in Mammography

AU - Rampun, Andrik

AU - Morrow, PJ

AU - Scotney, BW

AU - Winder, RJ

PY - 2017/5/21

Y1 - 2017/5/21

N2 - This paper presents a preliminary quantitative study for breast cancer risk assessment in mammography using mathematical operators called Local Ternary Patterns. The study covers three different mapping patterns namely uniform (‘u2’), nonuniform (‘ri’) and a combination of uniform and nonuniform (‘riu2’). These patterns are used as texture features to model the appearance of breast density within the fibroglandular disk area. Subsequently, the Support Vector Machine is employed as a classification approach and initial results suggest that the mapping pattern ‘riu2’ outperforms the others.

AB - This paper presents a preliminary quantitative study for breast cancer risk assessment in mammography using mathematical operators called Local Ternary Patterns. The study covers three different mapping patterns namely uniform (‘u2’), nonuniform (‘ri’) and a combination of uniform and nonuniform (‘riu2’). These patterns are used as texture features to model the appearance of breast density within the fibroglandular disk area. Subsequently, the Support Vector Machine is employed as a classification approach and initial results suggest that the mapping pattern ‘riu2’ outperforms the others.

KW - Computer Aided Diagnosis

KW - Local Ternary Patterns

KW - Breast Cancer and Mammography

U2 - 10.1007/978-3-319-59397-5_31

DO - 10.1007/978-3-319-59397-5_31

M3 - Conference contribution

SN - 978-3-319-59396-8

VL - 71

SP - 283

EP - 286

BT - Unknown Host Publication

PB - Springer

T2 - International Conference on Innovation in Medicine and Healthcare 2017

Y2 - 21 May 2017

ER -

A Quantitative Study of Local Ternary Patterns for Risk Assessment in Mammography

Abstract

Conference

Keywords

UN SDGs

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