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    A dissimilarity-based multiple classifier system for trabecular bone texture in detection and prediction of progression of knee osteoarthritis

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    Authors
    Woloszynski, Tomasz
    Podsiadlo, Pawel
    Stachowiak, Gwidon
    Kurzynski, M.
    Date
    2012
    Type
    Journal Article
    
    Metadata
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    Citation
    Woloszynski, Tomasz and Podsiadlo, Pawel and Stachowiak, Gwidon and Kurzynski, Marek. 2012. A dissimilarity-based multiple classifier system for trabecular bone texture in detection and prediction of progression of knee osteoarthritis. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine. 226 (11): pp. 887-894.
    Source Title
    Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine
    DOI
    10.1177/0954411912456650
    ISSN
    0954-4119
    URI
    http://hdl.handle.net/20.500.11937/39653
    Collection
    • Curtin Research Publications
    Abstract

    There is a growing need for classification systems that can accurately detect and predict knee osteoarthritis (OA) from plain radiographs. For this purpose, a system based on a support vector machine (SVM) classifier and distances measured between trabecular bone (TB) texture images was developed and tested in previous work. Unlike other systems, it allows an image classification without the calculation and selection of numerous texture features, and it is invariant to a range of imaging conditions encountered in a routine X-ray screening of knees. Although the system exhibited 85.4% classification accuracy in OA detection, which was higher than those obtained from other systems, its performance could be further improved. To achieve this, a dissimilarity-based multiple classifier (DMC) system is developed in this study. The system measures distances between TB texture images and generates a diverse ensemble of classifiers using prototype selection, bootstrapping of training set and heterogeneous classifiers. A measure of competence is used to select accurate (i.e. better-than-random) classifiers from the ensemble, which are then combined through the majority voting rule. To evaluate the newly developed system in OA detection (prediction of OA progression), TB texture images selected on standardised radiographs of healthy and OA (non-progressive and progressive OA) knees were used. The results obtained showed that the DMC system has higher classification accuracies for the detection (90.51% with 87.65% specificity and 93.33% sensitivity) and prediction (80% with 82.00% specificity and 77.97% sensitivity) than other systems, indicating its potential as a decision-support tool for the assessment of radiographic knee OA.

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    • Detection and prediction of osteoarthritis in knee and hand joints based on the X-ray image analysis
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      Woloszynski, Tomasz; Podsiadlo, Pawel; Kurzynski, M.; Stachowiak, Gwidon (2010)
      PURPOSE: The purpose of this study is to develop a dissimilarity measure for the classification of trabecular bone (TB) texture in knee radiographs. Problems associated with the traditional extraction and selection of ...
    • Prediction of progression of radiographic knee osteoarthritis using tibial trabecular bone texture
      Woloszynski, Tomasz; Podsiadlo, Pawel; Stachowiak, Gwidon; Kurzynski, M.; Lohmander, L.; Englund, M. (2012)
      Objective: To develop a system for predicting the progression of radiographic knee osteoarthritis (OA) using tibial trabecular bone texture. Methods: We studied 203 knees with (n = 68) or without (n = 135) radiographic ...
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