Bone Segmentation in Metacarpophalangeal MR Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3687)


A robust, efficient segmentation algorithm for automatic segmentation of MR images of the metacarpophalangeal joint is presented. A preliminary segmentation detects bones in MR scans and uses histogram analysis, morphological operations and knowledge based rules to classify various tissues in the joint. The second part of the algorithm improves the segmentation mask and refines boundaries of bones using minimization of a sum of square deviations, automatic signal segmentation into an optimum number of segments, graph theory, and statistical analysis. The algorithm has been tested on 9 MR patient studies and detects 97% of all existing bones correctly with an average exceeding 80% mutual overlap between ground truth and detected regions


Actual Boundary Magnetic Resonance Scan Signal Profile Boundary Pixel Magnetic Resonance Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    American College of Rheumatology of Osteoarthritis Guideline. Recommendations for the Medical Management of Osteoarthritis of the Hip and Knee. J. of Arthritis and Rheumatism 43, 1905–1915 (2000)Google Scholar
  2. 2.
    ANALYZE Software System, (last access May 26, 2005) and (last access May 26, 2005)
  3. 3.
    Bowyer, K.W.: Validation of Medical Image Analysis Techniques. In: Sonka, M., Fitz-patrick, J.M. (eds.) Handbook of Medical Imaging, pp. 567–607. SPIE Press, Bellingham (2000)Google Scholar
  4. 4.
    Golub, G.H., van Loan, C.F.: An Analysis of the Total Least Squares Problem SIAM. J. Numer. Anal. 17, 883–893 (1979)CrossRefGoogle Scholar
  5. 5.
    Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing, pp. 407–410. Prentice Hall, Englewood Cliffs (2004)Google Scholar
  6. 6.
    Hogg, R.V., Allen, T.C.: Introduction to Mathematical Statistics. Prentice Hall Publ., Englewood Cliffs (1994)Google Scholar
  7. 7.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models. Int. J. of Computer Vision 4, 321–331 (1987)Google Scholar
  8. 8.
    Kubassova, O., Boyle, R.D.: Segmentation of 4D Natural MR Images Based upon Morphological Image Analysis and Image Geometry. In: Proc. PREP 2005 Conference, Lancaster, UK, vol. 3, pp. 186–188 (2005)Google Scholar
  9. 9.
    Lim, K., Jae, S.: Two-Dimensional Signal and Image Processing, pp. 536–540. Prentice Hall, Englewood Cliffs (1990)Google Scholar
  10. 10.
    MatLab Image Processing User Guide Online, (last access May 25, 2005)
  11. 11.
    Ridler, T.W., Calvard, S.: Picture Thresholding Using an Iterative Selection Method. IEEE Trans. on Systems, Man and Cybernetics 8, 630–632 (1978)Google Scholar
  12. 12.
    Salvador, S., Chan, P.: Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms. In: Proc. 16th IEEE Intl. Conf. on Tools with AI, vol. 6, pp. 576–584 (2004)Google Scholar
  13. 13.
    Sonka, M., Hlavac, V., Boyle, R.D.: Image Processing Analysis, and Machine Vision. In: PWS, pp. 559–596 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  1. 1.School of ComputingUniversity of LeedsLeedsUK
  2. 2.BiophysicsRussian State UniversityMoscowRussia

Personalised recommendations