Fuzzy Model-based Algorithm for 3-D Bone Tumour Analysis

Joanna Czajkowska

Abstract

In this paper, a new fuzzy model based algorithm for 3-D bone tumour segmentation in MR series is introduced. The presented segmentation procedure is based on a modified fuzzy connectedness method. The there required fuzzy affinity values are estimated using a fuzzy inference system, whose fuzzy membership functions are structured on the basis of gaussian mixture model of analyzed image regions. The 3-D fuzzy tumour model is generated using different MR modalities acquired during a single examination. The segmentation abilities of prototype system have been tested on a MR database consisting of 27 examinations composed of two different sequences each.

References

  1. Badura, P., Kawa, J., Czajkowska, J., Rudzki, M., and Pietka, E. (2011). Fuzzy connectedness in segmentation of medical images. In International Conference of Fuzzy Computation Theory and Applications, pages 486-492.
  2. Carvalho, B. M., Joe Gau, C., Herman, G. T., and Yung Kong, T. (1999). Algorithms for Fuzzy Segmentation. Pattern Analysis & Applications, 2:73-81.
  3. Czajkowska, J., Bugdol, M., and Pietka, E. (2012). Kernelized fuzzy c-means method and gaussian mixture model in unsupervised cascade clustering. In International Conference of Information Technologies in Biomedicine, Lecture Notes in Bioinformatics, Gliwice, Poland, pages 58-66.
  4. Davies, A. M., Sundaram, M., and James, S. L. J. (2009). Imaging of Bone Tumors and Tumor-Like Lesions, Techniques and Applications. Medical Radiology, Diagnostic Imaging, Springer-Verlag Berlin Heidelberg, Berlin.
  5. Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1):1-38.
  6. Hata, Y., Kobashi, S., Hirano, S., Kitagaki, H., and Mori, E. (2000). Automated segmentation of human brain mr images aided by fuzzy information granulation and fuzzy inference. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 30(3):381 - 395.
  7. Heo, G. and Gader, P. (2010). An extension of global fuzzy c-means using kernel methods. In IEEE International Conference on Fuzzy Systems.
  8. Husband, J. E. and Reznek, R. H. (2004). Imaging in Oncology, volume 1. Taylor & Francis, London.
  9. Kickert, W. J. M. and Mamdani, E., H. (1978). Analysis of a fuzzy logic controller. Fuzzy Sets and Systems, 1(1):29 - 44.
  10. Ma, J., Li, M., and Zhao, Y. (2005). Segmentation of multimodality osteosarcoma mri with vectorial fuzzy-connectedness theory. Fuzzy Systems and Knowledge Discovery, Lecture Notes in Computer Science, Springer-Verlag Berlin Heidelberg, 36(14):1027-1030.
  11. Mari, M. and Dellepiane, S. (1996). A segmentation method based on fuzzy topology and clustering. In Pattern Recognition, 1996., Proceedings of the 13th International Conference on, volume 2, pages 565 -569 vol.2.
  12. McLachlan, G. and Peel, D. (2000). Finite Mixture Model. Wiley Series in Probability and Statistics.
  13. Pan, J. and Li, M. (2003). Segmentation of mr osteosarcoma images. In International Conference on Computational Intelligence and Multimedia Applications (ICCIMA03), IEEE.
  14. Pednekar, A., Kakadiaris, I. A., and Kurkure, U. (2008). Adaptive fuzzy connectedness-based medical image segmentation. In Proceedings of the Indian Conference on Computer Vision, Graphics, and Image Processing.
  15. Perona, P., Shiota, T., and Malik, J. (1994). Anisotropic diffusion. Geometry-Driven Diffusion in Computer VisionKluwer Academic Publishers, 3:73-92.
  16. Positano, V., Santarelli, M. F., Landin, L., and Benassi, A. (2000). Nonlinear anisotropic filtering as a tool for snr enhancement in cardiovascular mri. Computers in Cardiology, IEEE, pages 707-710.
  17. Rosenfeld, A. (1979). Fuzzy digital topology. Information and Control, 40(1):76-87.
  18. Saha, P. K. and Udupa, J. K. (2001). Fuzzy Connected Object Delineation: Axiomatic Path Strength Definition and the Case of Multiple Seeds. Computer Vision and Image Understanding, 83(3):275-295.
  19. Siler, W. and Buckley, J. J. (2005). Fuzzy Expert Systems and Fuzzy Reasoning. Wiley.
  20. Tolias, Y. and Panas, S. (1998). On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system. Signal Processing Letters, IEEE, 5(10):245 -247.
  21. Udupa, J. K., Saha, P. K., and Lotufo, R. A. (2002). Relative fuzzy connectedness and object definition: Theory, algorithms, and applications in image segmentation. IEEE Transaction on Pattern Analysis and Machine Intelligence, 24(11):1485-1500.
  22. Udupa, J. K. and Samarasekera, S. (1996). Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation. Graphical Models and Image Processing, 58(3):246-261.
  23. Yamaguchi, K., Fujimoto, Y., Kobashi, S., Wakata, Y., Ishikura, R., Kuramoto, K., Imawaki, S., Hirota, S., and Hata, Y. (2010). Automated fuzzy logic based skull stripping in neonatal and infantile mr images. In Fuzzy Systems (FUZZ), 2010 IEEE International Conference on, pages 1 -7.
  24. Zhao, Y., Hong, F., and Li, M. (2003). Multimodality mri information fusion for osteosarcoma segmentation. In Biomedical Engineering, 2003. IEEE EMBS AsianPacific Conference on, pages 166 - 167.
  25. Zhao, Y., Hong, F., and Li, M. (2004). Segmentation of osteosarcoma based on analysis of blood-perfusion epi series. In International Conference on Communications, Circuits and Systems, ICCCAS 2004, IEEE, volume 2.
Download


Paper Citation


in Harvard Style

Czajkowska J. (2013). Fuzzy Model-based Algorithm for 3-D Bone Tumour Analysis . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 185-192. DOI: 10.5220/0004498301850192


in Bibtex Style

@conference{fcta13,
author={Joanna Czajkowska},
title={Fuzzy Model-based Algorithm for 3-D Bone Tumour Analysis},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2013)},
year={2013},
pages={185-192},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004498301850192},
isbn={978-989-8565-77-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2013)
TI - Fuzzy Model-based Algorithm for 3-D Bone Tumour Analysis
SN - 978-989-8565-77-8
AU - Czajkowska J.
PY - 2013
SP - 185
EP - 192
DO - 10.5220/0004498301850192