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Proceedings Paper

Classification of micro-CT images using 3D characterization of bone canal patterns in human osteogenesis imperfecta
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Paper Abstract

Few studies have analyzed the microstructural properties of bone in cases of Osteogenenis Imperfecta (OI), or ‘brittle bone disease’. Current approaches mainly focus on bone mineral density measurements as an indirect indicator of bone strength and quality. It has been shown that bone strength would depend not only on composition but also structural organization. This study aims to characterize 3D structure of the cortical bone in high-resolution micro CT images. A total of 40 bone fragments from 28 subjects (13 with OI and 15 healthy controls) were imaged using micro tomography using a synchrotron light source (SRµCT). Minkowski functionals - volume, surface, curvature, and Euler characteristics - describing the topological organization of the bone were computed from the images. The features were used in a machine learning task to classify between healthy and OI bone. The best classification performance (mean AUC – 0.96) was achieved with a combined 4-dimensional feature of all Minkowski functionals. Individually, the best feature performance was seen using curvature (mean AUC - 0.85), which characterizes the edges within a binary object. These results show that quantitative analysis of cortical bone microstructure, in a computer-aided diagnostics framework, can be used to distinguish between healthy and OI bone with high accuracy.

Paper Details

Date Published: 3 March 2017
PDF: 8 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013413 (3 March 2017); doi: 10.1117/12.2254421
Show Author Affiliations
Anas Z. Abidin, Univ. of Rochester (United States)
John Jameson, Marquette Univ. (United States)
Robert Molthen, Marquette Univ. (United States)
Axel Wismüller, Univ. of Rochester (United States)
Faculty of Medicine and Institute of Radiology, Ludwig Maximilian Univ. Munich (Germany)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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