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International Conference Image Analysis and Recognition

ICIAR 2017: Image Analysis and Recognition pp 463–470Cite as

Breast Density Classification Using Local Ternary Patterns in Mammograms

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10317))

Abstract

This paper presents a method for breast density classification. Local ternary pattern operators are employed to model the appearance 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.

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Acknowledgments

This research was undertaken as part of the Decision Support and Information Management System for Breast Cancer (DESIREE) project. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 690238.

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Authors and Affiliations

  1. School of Computing and Information Engineering, Ulster University, Coleraine, BT52 1SA, Northern Ireland, UK

    Andrik Rampun, Philip Morrow & Bryan Scotney

  2. School of Health Sciences, Institute of Nursing and Health, Ulster University, Newtownabbey, BT37 0QB, Northern Ireland, UK

    John Winder

Authors
  1. Andrik Rampun
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  2. Philip Morrow
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  3. Bryan Scotney
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  4. John Winder
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Corresponding author

Correspondence to Andrik Rampun .

Editor information

Editors and Affiliations

  1. University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

  2. University of Porto, Porto, Portugal

    Aurélio Campilho

  3. Politechnique Montreal, Montreal, Québec, Canada

    Farida Cheriet

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Rampun, A., Morrow, P., Scotney, B., Winder, J. (2017). Breast Density Classification Using Local Ternary Patterns in Mammograms. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_51

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  • DOI: https://doi.org/10.1007/978-3-319-59876-5_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59875-8

  • Online ISBN: 978-3-319-59876-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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