Variation in segmentation of bone from micro-CT imaging: implications for quantitative morphometric analysis

Technical Report


Segmentation of bone in grey-level tomographs from micro-CT imaging is critical in determining the accuracy of morphometric analysis. The degree of variability in image segmentation between and within multiple operators will be quantified and compared with automated image segmentation. Three cubes of cancellous bone were cut from T12, L1, L3 and L4 human vertebral bodies (n=12). Micro-CT imaging was performed and a global threshold was determined by 3 operators independently and automatically using Otsu′s algorithm. Bone volume, trabecular thickness, trabecular separation, trabecular number, trabecular bone pattern factor, structure model index and degree of anisotropy were calculated. Percent bias and percent random error were calculated between all operators and Otsu’s method. For BV/TV, the maximum percent bias and percent random error were 22.0% and 11.3%, respectively, which constitutes differences in individual measurements between operators of up to 0.07. For Tb.Th, the maximum percent bias and percent random error were 13.1% and 6.4%, respectively, which constitutes differences in individual measurements between operators of up to 35μm. These data highlight to users of micro-CT imaging that morphometric analysis is highly sensitive to operating parameters. The effect on measurements of cancellous bone structure of different operators can be greater than experimental differences, which can lead to erroneous interpretation of results.

Key words

image segmentation micro-CT tomographs bias random error cancellous bone 


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  1. 1.
    Hildebrand, T. and Ruegsegger, P.A new method for the model-independent assessment of thickness in threedimensional images, J Microsc, 185:67–75, 1997.CrossRefGoogle Scholar
  2. 2.
    Odgaard, A.,Three-dimensional methods for quantification of cancellous bone architecture, Bone, 20:315–328, 1997.CrossRefPubMedGoogle Scholar
  3. 3.
    Hildebrand, T. and Ruegsegger, P.Quantification of bone microarchitecture with the structure model index, CMBBE, 1:15–23, 1997.PubMedGoogle Scholar
  4. 4.
    Whitehouse, W.J.,The quantitative morphology of anisotropic trabecular bone, J Microsc, 101:153–168, 1974.PubMedGoogle Scholar
  5. 5.
    Hipp, J.A., Jansujwicz, A., Simmons, C.A. and B.D. Snyder, Trabecular bone morphology from micro-magnetic resonance imaging, J Bone Miner Res, 11:286–297, 1996.PubMedGoogle Scholar
  6. 6.
    Ding, M., Odgaard, A. and Hvid, I.Accuracy of cancellous bone volume fraction measured by micro-CT scanning, J Biomech, 32:323–326, 1999.CrossRefPubMedGoogle Scholar
  7. 7.
    Vasilic, B. and Wehrli, F.W.A novel local thresholding algorithm for trabecular bone volume fraction mapping in the limited spatial resolution regime of in vivo MRI, IEEE Trans Med Imaging, 24:1574–1585, 2005.CrossRefPubMedGoogle Scholar
  8. 8.
    Batenburg, K.J. and Sijbers, J.Discrete tomography from micro-CT data: application to the mouse trabecular bone structure, Proc SPIE: Med Imaging: Phys Med Imaging, 6142:1325–1335, 2006.Google Scholar
  9. 9.
    Dufresne, T.E.,Segmentation techniques for analysis of bone by three-dimensional computed tomographic imaging, Technol Health Care, 6:351–359, 1998.PubMedGoogle Scholar
  10. 10.
    Rajagopalan, S., Yaszemski, M.J. and Robb, R.Evaluation of thresholding techniques for segmenting scaffold images in tissue engineering, Proc SPIE: Med Imaging: Image Process, 5370:1456–1465, 2004.Google Scholar
  11. 11.
    Stauber, M., Rapillard, L., van Lenthe, G.H., Zysset, P.K. and Muller, R.Importance of individual rods and plates in the assessment of bone quality and their contribution to the bone stiffness, J Bone Miner Res, 21:586–595, 2006.CrossRefPubMedGoogle Scholar
  12. 12.
    Parkinson, I.H. and Fazzalari, N.L.Cancellous bone structure analysis using image analysis, Australas Phys Eng Sci Med, 417:64–67, 1994.Google Scholar
  13. 13.
    Otsu, N.,A threshold selection method from gray-scale histogram, IEEE Trans Sys Man Cybern, 8:62–66, 1978.CrossRefGoogle Scholar
  14. 14.
    Hara, T., Tanck, E., Homminga, J. and Huiskes, R.The influence of microcomputed tomography threshold variations on the assessment of structural and mechanical trabecular bone properties, Bone, 31:107–109, 2002.CrossRefPubMedGoogle Scholar
  15. 15.
    Oh, W. and Lindquist, W.B.Image thresholding by indicator kriging, IEEE Trans Pattern Anal Mach Intell, 21:590–602, 1999.CrossRefGoogle Scholar
  16. 16.
    Waarsing, J.H., Day, J.S. and Weinans, H.An improved segmentation method for in vivo microCT imaging, J Bone Miner Res, 19:1640–1650, 2004.CrossRefPubMedGoogle Scholar
  17. 17.
    Feldkamp, L.A., Goldstein, S.A., Parfitt, A.M., Jesion, G. and Kleerekoper, M.The direct examination of three-dimensional bone architecture in vitro by computed tomography, J Bone Miner Res, 4:3–11, 1989.PubMedCrossRefGoogle Scholar
  18. 18.
    Hahn, M., Vogel, M., Popesius-Kempa, M. and Delling, G.Trabecular bone pattern factor: a new parameter for simple quantification of bone microarchitecture, Bone, 13:327–330, 1992.CrossRefPubMedGoogle Scholar
  19. 19.
    Borah, B., Defresne, T.E., Ritman, E.L., Jorgensen, J., Liu, S., Chmielewski, P.A., Phipps, R.J., Zhou, X., Sibonga, J.D. and Turner, R.T.Long-term risedronate treatment normalizes mineralization and continues to preserve trabecular architecture: sequential triple biopsy studies with microcomputed tomography, Bone, 39: 345–352. 2006.CrossRefPubMedGoogle Scholar
  20. 20.
    Chesnut, C.H., Majumdar, S., Newitt, D., Shields, A., van Pelt, J., Laschansky, E., Azria, M., Kriegman, A., Olson, M., Eriksen, E.F. and Mindeholm, L.Effects of salmon calcitonin on trabecular microarchitecture as determine by magnetic resonance imaging: results from the QUEST study, J Bone Miner Res, 20:1548–1561, 2005.CrossRefPubMedGoogle Scholar
  21. 21.
    Watts, N.B., Geusens, P., Barton, I.P. and Felsenberg, D.Relationship between changes in BMD and nonvertebral fracture incidence associated with risedronate: reduction in risk of nonvertebral fracture is not related to change in BMD, J Bone Miner Res, 20:2097–2104, 2005.CrossRefPubMedGoogle Scholar

Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2008

Authors and Affiliations

  1. 1.Bone and Joint Research Laboratory, Division of Tissue PathologyInstitute of Medical and Veterinary Science and Hanson InstituteAdelaideAustralia

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