Fritscher Karl, Schuler Benedikt, Link Thomas, Eckstein Felix, Suhm Norbert, Hänni Markus, Hengg Clemens, Schubert Rainer
Institute for Biomedical Image Analysis, UMIT, Austria.
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.
股骨近端骨折是老年人死亡的主要原因之一。传统的股骨骨折风险测定方法采用骨矿物质密度测量方法。然而，仅骨密度不足以预测个体患者的骨破坏负荷，为此必须确定其他参数。在这项工作中，将提出一种利用外观统计模型来识别相关区域和参数以预测股骨近端生物力学特性的方法。通过使用支持向量回归，所提出的基于模型的方法能够在两种不同的测试场景中准确且全自动地预测两个不同的生物力学参数。
