Breast Density Classification using Local Ternary Patterns in Mammograms

Andrik Rampun, Philip Morrow, Bryan Scotney, John Winder

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

7 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 languageEnglish
Title of host publicationUnknown Host Publication
PublisherSpringer
Pages463-470
Number of pages8
Volume10317
ISBN (Print)978-3-319-59876-5
DOIs
Publication statusE-pub ahead of print - 2 Jun 2017
EventImage Analysis and Recognition 14th International Conference, ICIAR 2017, Montreal, QC, Canada, July 5–7, 2017 - Montreal, Canada
Duration: 2 Jun 2017 → …

Conference

ConferenceImage Analysis and Recognition 14th International Conference, ICIAR 2017, Montreal, QC, Canada, July 5–7, 2017
Period2/06/17 → …

Keywords

  • Mammography
  • breast density
  • local ternary patterns
  • classification

Fingerprint Dive into the research topics of 'Breast Density Classification using Local Ternary Patterns in Mammograms'. Together they form a unique fingerprint.

  • Cite this

    Rampun, A., Morrow, P., Scotney, B., & Winder, J. (2017). Breast Density Classification using Local Ternary Patterns in Mammograms. In Unknown Host Publication (Vol. 10317, pp. 463-470). Springer. https://doi.org/10.1007/978-3-319-59876-5