Classification of Osteoporotic Vertebral Fractures Using Shape and Appearance Modelling

  • Paul A. BromileyEmail author
  • Eleni P. Kariki
  • Judith E. Adams
  • Timothy F. Cootes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10734)


Osteoporotic vertebral fractures (VFs) are under-diagnosed, creating an opportunity for computer-aided, opportunistic fracture identification in clinical images. VF diagnosis and grading in clinical practice involves comparisons of vertebral body heights. However, machine vision systems can provide a high-resolution segmentation of the vertebrae and fully characterise their shape and appearance, potentially allowing improved diagnostic accuracy. We compare approaches based on vertebral heights to shape/appearance modelling combined with k-nearest neighbours and random forest (RF) classifiers, on both dual-energy X-ray absorptiometry images and computed tomography image volumes. We demonstrate that the combination of RF classifiers and appearance modelling, which is novel in this application, results in a significant (up to 60% reduction in false positive rate at 80% sensitivity) improvement in diagnostic accuracy.


Osteoporosis Vertebral fracture Shape modelling 



This publication presents independent research supported by the Health Innovation Challenge Fund (grant no. HICF-R7-414/WT100936), a parallel funding partnership between the Department of Health and Wellcome Trust, and by the NIHR Invention for Innovation (i4i) programme (grant no. II-LB_0216-20009). The views expressed are those of the authors and not necessarily those of the NHS, NIHR, the Department of Health or Wellcome Trust. The authors acknowledge the invaluable assistance of Mrs Chrissie Alsop, Mr Stephen Capener, Mrs Imelda Hodgkinson, Mr Michael Machin, and Mrs Sue Roberts, who performed the manual annotations.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Paul A. Bromiley
    • 1
    Email author
  • Eleni P. Kariki
    • 2
  • Judith E. Adams
    • 2
  • Timothy F. Cootes
    • 1
  1. 1.Centre for Imaging Sciences, School of Health SciencesUniversity of ManchesterManchesterUK
  2. 2.Radiology and Manchester Academic Health Science CentreCentral Manchester University Hospitals NHS Foundation TrustManchesterUK

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