Quantification and 3D Visualization of Articular Cartilage of Knee Joint Using Image Processing Techniques

  • M. S. Mallikarjunaswamy
  • Mallikarjun S. Holi
  • Rajesh Raman
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 32)

Abstract

The articular cartilage of knee joint plays an important role in smooth movement and lubrication of the joint. Osteoarthritis (OA) is a degenerative disease of the knee joint, commonly affecting the elderly around the world. Visualization and morphological analysis of cartilage plays an important role in the assessment of OA and rehabilitate the affected people. In the present work, thickness and volume of the cartilage was quantified using an edge detection based interactive segmentation method from knee joint magnetic resonance images (MRI) and the joint is visualized in 3D. Volume of interest (VOI) processing approach was used to reduce the number of voxels processed in 3D rendering of articular cartilage. The method reduces the processing time in comparison with manual and other semiautomatic methods. The agreement of thickness and volume measurements was assessed using Bland-Altman plots in comparison with manual method.

Keywords

Cartilage volume Interactive segmentation 3D visualization MRI Osteoarthritis 

Notes

Acknowledgments

Osteoarthritis Initiative (OAI), National Institute of Health, USA for providing knee MR images.

References

  1. 1.
    Levangie, P.K., Norkin, C.C.: Joint structure and function: a comprehensive analysis, 4th edn. F.A. Davis Company, Philadephia (2006)Google Scholar
  2. 2.
    Joshi, J., Kotwal, P.: Essentials of orthopedics and applied physiotherapy, 1st edn. Elsevier, New Delhi (2008)Google Scholar
  3. 3.
    Cohen, Z.A., McCarty, D.M., Kwak, S.D., Legrand, P., Fogarasi, F., Ciaccio, E.J., Ateshian, G.A.: Knee cartilage topography, thickness, and contact areas from MRI: in-vitro calibration and in-vivo measurements. Osteoarthritis Cartilage 7, 95–109 (1999)Google Scholar
  4. 4.
    Cashman, P.M.M., Kitney, R.I., Gariba, M.A., Carter, M.E.: Automated techniques for visualization and mapping of articular cartilage in MR images of the osteoarthritic knee: a base technique for the assessment of microdamage and submicro damage. IEEE Trans. Nanobiosci. 1, 42–51 (2002)Google Scholar
  5. 5.
    Poh, C.L., Kitney, R.I.: Viewing interfaces for segmentation and measurement results. In: Proceedings of 27th Annual Conference on IEEE Engineering in Medicine and Biology, Shanghai, China, pp. 5132–5135 (2005)Google Scholar
  6. 6.
    Folkesson, J., Dam, E.B., Olsen, O.F., Pettersen, P.C., Christiansen, C.: Segmenting articular cartilage automatically using a voxel classification approach. IEEE Trans. Medi. Imaging 26, 106–115 (2007)Google Scholar
  7. 7.
    Kauffmann, C., Gravel, P., Godbout, B., Gravel, A., Beaudoin, G., Raynauld, J.-P., Pelletier, J.M., de Guise, J.A.: Computer-aided method for quantification of cartilage thickness and volume changes using MRI: validation study using a synthetic model. IEEE Trans. Biomed. Eng. 50, 978–988 (2003)Google Scholar
  8. 8.
    Tang, J., Millington, S., Acton, S.T., Crandall, J., Hurwitz, S.: Surface extraction and thickness measurement of the articular cartilage from MR images using directional gradient vector flow snakes. IEEE Trans. Biomed. Eng. 53, 896–907 (2006)CrossRefGoogle Scholar
  9. 9.
    Anastasi, G., Bramanti, P., Di Bella, P., Favaloro, A., Trimarchi, F., Magaudda, L., Gaeta, M., Scribano, E., Bruschetta, D., Milardi, D.: Volume rendering based on magnetic resonance imaging: advances in understanding the three-dimensional anatomy of the human knee. J. Anat. 211, 399–406 (2007)CrossRefGoogle Scholar
  10. 10.
    Udupa, J.K., Herman, G.T.: 3D imaging in medicine, 2nd edn. CRC Press, Boca Raton (1999)Google Scholar
  11. 11.
    Baysal, O., Baysal, T., Alkan, A, Altay, Z., Yologlu, S.: Comparison of MRI graded cartilage and MRI based volume measurement in knee osteoarthritis. Swiss Med. Weekly 134, 283–288 (2004)Google Scholar
  12. 12.
    Swamy, M.S.M., Holi, M.S.: Segmentation, visualization and quantification of knee joint articular cartilage using MR images. In: Swamy, P.P., Guru, D.S. (eds), Multimedia Processing, Communication and Computing Applications. LNEE, vol. 213, pp. 321–332. Springer, Berlin (2013)Google Scholar
  13. 13.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698 (1986)CrossRefGoogle Scholar
  14. 14.
    Altman, D.G., Bland, J.M.: Measurement in medicine: the analysis of method comparison studies. Statistician 32, 307–337 (1983)CrossRefGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • M. S. Mallikarjunaswamy
    • 1
    • 2
  • Mallikarjun S. Holi
    • 3
  • Rajesh Raman
    • 4
  1. 1.Department of Biomedical EngineeringBIETDavangereIndia
  2. 2.Department of Instrumentation TechnologyS.J. College of EngineeringMysoreIndia
  3. 3.Department of Electronics and Instrumentation Engineering, University B.D.T. College of EngineeringVisvesvaraya Technological UniversityDavangereIndia
  4. 4.Department of Radio-Diagnosis, J.S.S. Medical CollegeJSS UniversityMysoreIndia

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