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Proceedings Paper

Characterizing trabecular bone structure for assessing vertebral fracture risk on volumetric quantitative computed tomography
Author(s): Mahesh B. Nagarajan; Walter A. Checefsky; Anas Z. Abidin; Halley Tsai; Xixi Wang; Susan K. Hobbs; Jan S. Bauer; Thomas Baum; Axel Wismüller
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Paper Abstract

While the proximal femur is preferred for measuring bone mineral density (BMD) in fracture risk estimation, the introduction of volumetric quantitative computed tomography has revealed stronger associations between BMD and spinal fracture status. In this study, we propose to capture properties of trabecular bone structure in spinal vertebrae with advanced second-order statistical features for purposes of fracture risk assessment. For this purpose, axial multi-detector CT (MDCT) images were acquired from 28 spinal vertebrae specimens using a whole-body 256-row CT scanner with a dedicated calibration phantom. A semi-automated method was used to annotate the trabecular compartment in the central vertebral slice with a circular region of interest (ROI) to exclude cortical bone; pixels within were converted to values indicative of BMD. Six second-order statistical features derived from gray-level co-occurrence matrices (GLCM) and the mean BMD within the ROI were then extracted and used in conjunction with a generalized radial basis functions (GRBF) neural network to predict the failure load of the specimens; true failure load was measured through biomechanical testing. Prediction performance was evaluated with a root-mean-square error (RMSE) metric. The best prediction performance was observed with GLCM feature ‘correlation’ (RMSE = 1.02 ± 0.18), which significantly outperformed all other GLCM features (p < 0.01). GLCM feature correlation also significantly outperformed MDCTmeasured mean BMD (RMSE = 1.11 ± 0.17) (p< 10-4). These results suggest that biomechanical strength prediction in spinal vertebrae can be significantly improved through characterization of trabecular bone structure with GLCM-derived texture features.

Paper Details

Date Published: 17 March 2015
PDF: 8 pages
Proc. SPIE 9417, Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging, 94171E (17 March 2015); doi: 10.1117/12.2082059
Show Author Affiliations
Mahesh B. Nagarajan, Univ. of Rochester (United States)
Walter A. Checefsky, Univ. of Rochester (United States)
Anas Z. Abidin, Univ. of Rochester (United States)
Halley Tsai, Univ. of Rochester (United States)
Xixi Wang, Univ. of Rochester (United States)
Susan K. Hobbs, Univ. of Rochester (United States)
Jan S. Bauer, Technische Univ. München (Germany)
Thomas Baum, Technische Univ. München (Germany)
Axel Wismüller, Univ. of Rochester (United States)
Ludwig Maximilian Univ. (Germany)

Published in SPIE Proceedings Vol. 9417:
Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Robert C. Molthen, Editor(s)

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