Breast density classification in mammograms: An investigation of encoding techniques in binary-based local patterns

Andrik Rampun*, Philip J. Morrow, Bryan W. Scotney, Hui Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

We investigate various channel encoding techniques applied to breast density classification in mammograms; specifically, local binary, ternary, and quinary encoding approaches are considered. Subsequently, we propose a new encoding approach based on a seven-encoding technique, yielding a new local pattern operator called a local septenary pattern operator. Experimental results suggest that the proposed local pattern operator is robust and outperforms the other encoding techniques when evaluated on the Mammographic Image Analysis Society (MIAS) and InBreast datasets. The local septenary pattern operator achieved a maximum classification accuracy of 83.3% and 80.5% on the MIAS and InBreast datasets, respectively. The closest comparison achieved by the other local pattern operators is the local quinary operator, with maximum accuracies of 82.1% (MIAS) and 80.1% (InBreast), respectively.

Original languageEnglish
Article number103842
JournalComputers in Biology and Medicine
Volume122
Early online date03 Jun 2020
DOIs
Publication statusPublished - Jul 2020

Bibliographical note

Funding Information:
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 .

Funding Information:
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.

Publisher Copyright:
© 2020

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Breast density
  • Breast mammography
  • Local binary patterns
  • Local quinary patterns
  • Local septenary patterns
  • Local ternary patterns

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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