Predicting the biomechanical strength of proximal femur specimens with bone mineral density features and support vector regression
2012
To improve the clinical assessment of osteoporotic hip fracture risk, recent computer-aided diagnosis systems
explore new approaches to estimate the local trabecular bone quality beyond bone density alone to predict femoral
bone strength. In this context, statistical bone mineral density (BMD) features extracted from multi-detector
computed tomography (MDCT) images of proximal femur specimens and different function approximations
methods were compared in their ability to predict the biomechanical strength. MDCT scans were acquired in
146 proximal femur specimens harvested from human cadavers. The femurs' failure load (FL) was determined
through biomechanical testing. An automated volume of interest (VOI)-fitting algorithm was used to define a
consistent volume in the femoral head of each specimen. In these VOIs, the trabecular bone was represented
by statistical moments of the BMD distribution and by pairwise spatial occurrence of BMD values using the
gray-level co-occurrence (GLCM) approach. A linear multi-regression analysis (MultiReg) and a support vector
regression algorithm with a linear kernel (SVRlin) were used to predict the FL from the image feature sets.
The prediction performance was measured by the root mean square error (RMSE) for each image feature on
independent test sets; in addition the coefficient of determination R 2 was calculated. The best prediction
result was obtained with a GLCM feature set using SVRlin, which had the lowest prediction error (RSME =
1.040±0.143, R 2 = 0.544) and which was significantly lower that the standard approach of using BMD.mean and
MultiReg (RSME = 1.093±0.133, R 2 = 0.490, p<0.0001). The combined sets including BMD.mean and GLCM
features had a similar or slightly lower performance than using only GLCM features. The results indicate that the
performance of high-dimensional BMD features extracted from MDCT images in predicting the biomechanical
strength of proximal femur specimens can be significantly improved by using support vector regression.
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