Prediction of biomechanical parameters of the proximal femur using statistical appearance models and support vector regression

Med Image Comput Comput Assist Interv. 2008;11(Pt 1):568-75. doi: 10.1007/978-3-540-85988-8_68.


Fractures of the proximal femur are one of the principal causes of mortality among elderly persons. Traditional methods for the determination of femoral fracture risk use methods for measuring bone mineral density. However, BMD alone is not sufficient to predict bone failure load for an individual patient and additional parameters have to be determined for this purpose. In this work an approach that uses statistical models of appearance to identify relevant regions and parameters for the prediction of biomechanical properties of the proximal femur will be presented. By using Support Vector Regression the proposed model based approach is capable of predicting two different biomechanical parameters accurately and fully automatically in two different testing scenarios.

MeSH terms

  • Absorptiometry, Photon / methods
  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms
  • Artificial Intelligence*
  • Biomechanical Phenomena
  • Bone Density / physiology*
  • Computer Simulation
  • Female
  • Femur Head / diagnostic imaging*
  • Femur Head / physiology*
  • Humans
  • Male
  • Middle Aged
  • Models, Biological*
  • Models, Statistical
  • Pattern Recognition, Automated / methods*
  • Radiographic Image Enhancement / methods
  • Radiographic Image Interpretation, Computer-Assisted / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Tomography, X-Ray Computed / methods