Predicting the biomechanical strength of proximal femur specimens with bone mineral density features and support vector regression

Markus B. Huber; Chien-Chun Yang; Julio Carballido-Gamio; Jan S. Bauer; Thomas Baum; Mahesh B. Nagarajan; Felix Eckstein; Eva Lochmüller; Sharmila Majumdar; Thomas M. Link; Axel Wismüller

doi:10.1117/12.911402

23 February 2012 Predicting the biomechanical strength of proximal femur specimens with bone mineral density features and support vector regression

Markus B. Huber, Chien-Chun Yang, Julio Carballido-Gamio, Jan S. Bauer, Thomas Baum, Mahesh B. Nagarajan, Felix Eckstein, Eva Lochmüller, Sharmila Majumdar, Thomas M. Link, Axel Wismüller

Author Affiliations +

Proceedings Volume 8315, Medical Imaging 2012: Computer-Aided Diagnosis; 83151W (2012) https://doi.org/10.1117/12.911402
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

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 R² 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, R² = 0.544) and which was significantly lower that the standard approach of using BMD.mean and MultiReg (RSME = 1.093±0.133, R² = 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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Markus B. Huber, Chien-Chun Yang, Julio Carballido-Gamio, Jan S. Bauer, Thomas Baum, Mahesh B. Nagarajan, Felix Eckstein, Eva Lochmüller, Sharmila Majumdar, Thomas M. Link, and Axel Wismüller "Predicting the biomechanical strength of proximal femur specimens with bone mineral density features and support vector regression", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83151W (23 February 2012); https://doi.org/10.1117/12.911402

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