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Automated Quantitative Bone Analysis in In Vivo X-ray Micro-Computed Tomography.

Authors
  • Behrooz, Ali
  • Kask, Peet
  • Meganck, Jeff
  • Kempner, Joshua
Type
Published Article
Journal
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Sep 01, 2017
Volume
36
Issue
9
Pages
1955–1965
Identifiers
DOI: 10.1109/TMI.2017.2712571
PMID: 28600241
Source
Medline
License
Unknown

Abstract

Measurement and analysis of bone morphometry in 3D micro-computed tomography volumes using automated image processing and analysis improve the accuracy, consistency, reproducibility, and speed of preclinical osteological research studies. Automating segmentation and separation of individual bones in 3D micro-computed tomography volumes of murine models presents significant challenges considering partial volume effects and joints with thin spacing, i.e., 50 to [Formula: see text]. In this paper, novel hybrid splitting filters are presented to overcome the challenge of automated bone separation. This is achieved by enhancing joint contrast using rotationally invariant second-derivative operators. These filters generate split components that seed marker-controlled watershed segmentation. In addition, these filters can be used to separate metaphysis and epiphysis in long bones, e.g., femur, and remove the metaphyseal growth plate from the detected bone mask in morphometric measurements. Moreover, for slice-by-slice stereological measurements of long bones, particularly curved bones, such as tibia, the accuracy of the analysis can be improved if the planar measurements are guided to follow the longitudinal direction of the bone. In this paper, an approach is presented for characterizing the bone medial axis using morphological thinning and centerline operations. Building upon the medial axis, a novel framework is presented to automatically guide stereological measurements of long bones and enhance measurement accuracy and consistency. These image processing and analysis approaches are combined in an automated streamlined software workflow and applied to a range of in vivo micro-computed tomography studies for validation.

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