Wear debris from total joint replacements: evaluation of automated categorisation by scale-invariant feature transforms

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Abstract

Wear debris is a crucial factor in determining the lifespan of a total joint replacement. Not only do particulates between a bearing surfaces increase wear rates through third-body abrasion, but immune reactions can lead to inflammation and osteolysis. In this paper, the use of computer vision to analyse and classify scanning electron microscope images of debris was investigated. UHMWPE debris was generated using an in vitro simulator or a linear tribometer, images were analysed using scale invariant feature transforms and a support vector machine classifier. The accuracy was 77.6% with a receiver operating characteristic area under curve of 92%.
Original languageEnglish
Number of pages10
JournalComputer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
Early online date7 Nov 2016
DOIs
Publication statusE-pub ahead of print - 7 Nov 2016

Keywords

  • Total disc arthroplasty;
  • Polymer wear debris;
  • SVM;
  • SIFT;
  • Machine learning;

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