Breast Density Classification Using Local Quinary Patterns with Various Neighbourhood Topologies

Andrik Rampun, Bryan Scotney, Philip Morrow, Hui Wang, John Winder

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)
75 Downloads (Pure)


This paper presents an extension of work from our previous study by investigating the use of Local Quinary Patterns (LQP) for breast density classification in mammograms on various neighbourhood topologies. The LQP operators are used to capture the texture characteristics of the fibro-glandular disk region (FGDroi) instead of the whole breast area as the majority of current studies have done. We take a multiresolution and multi-orientation approach, investigate the effects of various neighbourhood topologies and select dominant patterns to maximise texture information. Subsequently, 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 produced competitive results up to 86.13% and 82.02% accuracy based on 322 and 206 mammograms taken from the Mammographic Image Analysis Society (MIAS) and InBreast datasets, which is comparable with the state-of-the-art in the literature.
Original languageEnglish
Pages (from-to)1-23
Number of pages23
JournalJournal of Imaging
Issue number1
Publication statusPublished (in print/issue) - 8 Jan 2018


  • breast density classification
  • computer aided diagnosis
  • local quinary patterns
  • breast mammography


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