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Proceedings Paper

Predicting the biomechanical strength of proximal femur specimens through high dimensional geometric features and support vector regression
Author(s): Chien-Chun Yang; Mahesh B. Nagarajan; Markus B. Huber; Julio Carballido-Gamio; Jan S. Bauer; Thomas Baum; Felix Eckstein; Eva Lochmüller; Sharmila Majumdar; Thomas M. Link; Axel Wismüller
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Paper Abstract

Estimating local trabecular bone quality for purposes of femoral bone strength prediction is important for improving the clinical assessment of osteoporotic hip fracture risk. In this study, we explore the ability of geometric features derived from the Scaling Index Method (SIM) in predicting the biomechanical strength of proximal femur specimens as visualized on multi-detector computed tomography (MDCT) images. MDCT scans were acquired for 50 proximal femur specimens harvested from human cadavers. 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 non-linear micro-structure of the trabecular bone was characterized by statistical moments of its BMD distribution and by local scaling properties derived from SIM. Linear multi-regression analysis and support vector regression with a linear kernel (SVRlin) were used to predict the failure load (FL) from the feature sets; the predicted FL was compared to the FL values determined through biomechanical testing. The prediction performance was measured by the root mean square error (RMSE) for each image feature on independent test set. The best prediction result was obtained from the SIM feature set with SVRlin, which had the lowest prediction error (RMSE = 0.842 ± 0.209) and which was significantly lower than the conventionally used mean BMD (RMSE = 1.103 ± 0.262, , p<0.005). Our results indicate that the biomechanical strength prediction can be significantly improved in proximal femur specimens on MDCT images by using high-dimensional geometric features derived from SIM with support vector regression.

Paper Details

Date Published: 29 March 2013
PDF: 9 pages
Proc. SPIE 8672, Medical Imaging 2013: Biomedical Applications in Molecular, Structural, and Functional Imaging, 86721K (29 March 2013); doi: 10.1117/12.2006265
Show Author Affiliations
Chien-Chun Yang, Univ. of Rochester (United States)
Mahesh B. Nagarajan, Univ. of Rochester (United States)
Markus B. Huber, Univ. of Rochester (United States)
Julio Carballido-Gamio, Univ. of California, San Francisco (United States)
Jan S. Bauer, Technische Univ. München (Germany)
Thomas Baum, Technische Univ. München (Germany)
Felix Eckstein, Paracelsus Medical Univ. Salzburg (Austria)
Eva Lochmüller, Paracelsus Medical Univ. Salzburg (Austria)
Sharmila Majumdar, Univ. of California, San Francisco (United States)
Thomas M. Link, Univ. of California, San Francisco (United States)
Axel Wismüller, Univ. of Rochester (United States)
Ludwig Maximilians Univ. Munich (Germany)

Published in SPIE Proceedings Vol. 8672:
Medical Imaging 2013: Biomedical Applications in Molecular, Structural, and Functional Imaging
John B. Weaver; Robert C. Molthen, Editor(s)

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