Prediction of Biomechanical Parameters of the Proximal Femur Using Statistical Appearance Models and Support Vector Regression

  • Authors:

  • Karl Fritscher;Benedikt Schuler;Thomas Link;Felix Eckstein;Norbert Suhm;Markus Hänni;Clemens Hengg;Rainer Schubert

  • Affiliations:

  • Institute for Biomedical Image Analysis, UMIT, Austria;Institute for Biomedical Image Analysis, UMIT, Austria;Department of Radiology and Biomedical Imaging, University of California, San Francisco,;Institut für Anatomie und muskolosekelttale Forschung, Paracelsus University, Salzburg,;AO Development Institute, , Davos, Switzerland;AO Development Institute, , Davos, Switzerland;Department of Trauma Surgery, Medical University Innsbruck, Austria;Institute for Biomedical Image Analysis, UMIT, Austria

  • Venue:
  • MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
  • Year:
  • 2008

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Abstract

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.