The authors have no conflict of interest
An Improved Segmentation Method for In Vivo μCT Imaging†
Article first published online: 12 JUL 2004
Copyright © 2004 ASBMR
Journal of Bone and Mineral Research
Volume 19, Issue 10, pages 1640–1650, October 2004
How to Cite
Waarsing, J. H., Day, J. S. and Weinans, H. (2004), An Improved Segmentation Method for In Vivo μCT Imaging. J Bone Miner Res, 19: 1640–1650. doi: 10.1359/JBMR.040705
- Issue published online: 2 DEC 2009
- Article first published online: 12 JUL 2004
- Manuscript Accepted: 21 MAY 2004
- Manuscript Revised: 3 MAY 2004
- Manuscript Received: 19 NOV 2003
Image segmentation methods for μCT can influence the accuracy of bone morphometry calculations. A new automated segmentation method is introduced, and its performance is compared with standard segmentation methods. The new method can improve the results of in vivo μCT, where the need to keep radiation dose low limits scan quality.
Introduction: An important topic for μCT analysis of bone samples is the segmentation of the original reconstructed grayscale data sets to separate bone from non-bone. Problems like noise, resolution limitations, and beam-hardening make this a nontrivial issue. Inappropriate segmentation methods will reduce the potential power of μCT and may introduce bias in the architectural measurements, in particular, when new in vivo μCT with its inherent limitations in scan quality is used. Here we introduce a new segmentation method using local thresholds and compare its performance to standard global segmentation methods.
Material and Methods: The local threshold method was validated by comparing the result of the segmentation with histology. Furthermore, the effect of choosing this new method versus standard segmentation methods using global threshold values was investigated by studying the sensitivity of these methods to signal to noise ratio and resolution.
Results: Using the new method on high-quality scans yielded accurate results and virtually no differences between histology and the segmented data sets could be observed. When prior knowledge about the volume fraction of the bone was available the global threshold also resulted in appropriate results. Degrading the scan quality had only minor effects on the performance of the new segmentation method. Although global segmentation methods were not sensitive to noise, it was not possible to segment both lower mineralized thin trabeculae and the higher mineralized cortex correctly with the same threshold value.
Conclusion: At high resolutions, both the new local and conventional global segmentation methods gave near exact representations of the bone structure. When scanned samples are not homogenous (e.g., thick cortices and thin trabeculae) and when resolution is relatively low, the local segmentation method outperforms global methods. It maximizes the potential of in vivo μCT by giving good structural representation without the need to use longer scanning times that would increase absorption of harmful X-ray radiation by the living tissue.