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

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@article{9a0ae1e0acd842f8a15b2af7ecd1fed3,
title = "Wear debris from total joint replacements:: evaluation of automated categorisation by scale-invariant feature transforms",
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%.",
keywords = "Total disc arthroplasty; , Polymer wear debris; , SVM; , SIFT; , Machine learning;",
author = "David Eckold and Karl Dearn and Duncan Shepherd",
year = "2016",
month = nov,
day = "7",
doi = "10.1080/21681163.2016.1230075",
language = "English",
journal = "Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization",
issn = "2168-1163",
publisher = "Taylor & Francis",

}

RIS

TY - JOUR

T1 - Wear debris from total joint replacements:

T2 - evaluation of automated categorisation by scale-invariant feature transforms

AU - Eckold, David

AU - Dearn, Karl

AU - Shepherd, Duncan

PY - 2016/11/7

Y1 - 2016/11/7

N2 - 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%.

AB - 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%.

KW - Total disc arthroplasty;

KW - Polymer wear debris;

KW - SVM;

KW - SIFT;

KW - Machine learning;

U2 - 10.1080/21681163.2016.1230075

DO - 10.1080/21681163.2016.1230075

M3 - Article

JO - Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization

JF - Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization

SN - 2168-1163

ER -