An intelligent system for image-based rating of corrosion severity at stem taper of retrieved hip replacement implants

Roohollah Milimonfared, Reza Hashemi Oskouei, Mark Taylor, Lucian Solomon

    Research output: Contribution to journalArticle

    3 Citations (Scopus)

    Abstract

    Visual scoring of damage at taper junctions is the sole method to quantify corrosion in large-scale retrieval studies of failed hip replacement implants. This study introduces an intelligent image analysis-based method that objectively rates corrosion at stem taper of retrieved hip implants according to the well-known Goldberg scoring method. A Support Vector Machine classifier was used that takes in vectors of global and local textural features and assigns scores to the corresponding images. Bayesian optimisation fine-tunes the hyperparameters of the classifier to minimise the cross-validation error.


    Original languageEnglish
    Pages (from-to)13-24
    Number of pages12
    JournalMedical Engineering and Physics
    Volume61
    DOIs
    Publication statusPublished - Nov 2018

    Keywords

    • Total hip arthroplasty
    • Metallic implants
    • Machine learning
    • Digital image processing
    • Texture analysis

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