Breast Density Classification in Mammograms: An Investigation of Encoding Techniques in Binary-based Local Patterns

Andrik Bin Rampun, PJ Morrow, Bryan Scotney, H. Wang

Research output: Contribution to journalArticle

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
Number of pages18
JournalComputers in Biology and Medicine
Volume122
Early online date3 Jun 2020
DOIs
Publication statusPublished - 31 Jul 2020

Keywords

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

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