[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

A New Parallel Approach to Fuzzy Clustering for Medical Image Segmentation

  • Conference paper
Advances in Visual Computing (ISVC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5358))

Included in the following conference series:

  • 2431 Accesses

Abstract

Medical image segmentation plays an important role in medical image analysis and visualization. The Fuzzy c-Means (FCM) is one of the well-known methods in the practical applications of medical image segmentation. FCM, however, demands tremendous computational throughput and memory requirements due to a clustering process in which the pixels are classified into the attributed regions based on the global information of gray level distribution and spatial connectivity. In this paper, we present a parallel implementation of FCM using a representative data parallel architecture to overcome computational requirements as well as to create an intelligent system for medical image segmentation. Experimental results indicate that our parallel approach achieves a speedup of 1000x over the existing faster FCM method and provides reliable and efficient processing on CT and MRI image segmentation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Fu, K.S., Mu, J.K.: A Survey on Image Segmentation. Pattern Recognition 13, 3–16 (1983)

    Article  MathSciNet  Google Scholar 

  2. Sahoo, P.K., Soltani, S., Wong, A.K.C., Chen, Y.C.: A Survey of Thresholding Techniques. CVGIP 41, 233–260 (1988)

    Google Scholar 

  3. Panda, D.P., Rosenfeld, A.: Image Segmentation by Pixel Classification in (Gray Level, Edge Value) space. IEEE Transactions on Computers 22, 440–450 (1975)

    Google Scholar 

  4. Hall, L.O., Bensaid, A.M., Clarke, L.P., Velthuizen, R.P., Silbiger, M.S., Bezdek, J.C.: A Comparison of Neural Network and Fuzzy Clustering Techniques in Segmenting Magnetic Resonance Images of the Brain. IEEE Transactions on Neural Networks 3, 672–682 (1992)

    Article  Google Scholar 

  5. Kim, Y., Rajala, S.A., Snyder, W.E.: Image Segmentation using an Annealed Hopfield Neural Network. In: Proc. RNNS/IEEE Symp. Neural Informatics and Neurocomputers, vol. 1, pp. 311–322 (1992)

    Google Scholar 

  6. Tabakov, M.: A Fuzzy Clustering Technique for Medical Image Segmentation. In: International Symposium on Evolving Fuzzy Systems, pp. 118–122 (2006)

    Google Scholar 

  7. Dunn, J.C.: A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics 3, 32–57 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  8. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)

    Book  MATH  Google Scholar 

  9. Sahaphong, S., Hiransakolwong, N.: Unsupervised Image Segmentation Using Automated Fuzzy c-Means. In: 7th IEEE International Conference on Computer and Information Technology, pp. 690–694 (2007)

    Google Scholar 

  10. Kim, J., Wills, D.S., Wills, L.M.: Implementing and Evaluating Color-Aware Instruction Set for Low-Memory, Embedded Video Processing in Data Parallel Architectures. In: Yang, L.T., Amamiya, M., Liu, Z., Guo, M., Rammig, F.J. (eds.) EUC 2005. LNCS, vol. 3824, pp. 4–16. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  11. Rahimi, S., Zargham, M., Thakre, A., Chhillar, D.: A parallel Fuzzy C-Mean algorithm for image segmentation. IEEE Annual Metting on Fuzzy Information 1, 234–237 (2004)

    Google Scholar 

  12. Wu, J., Li, J., Liu, J., Tian, J.: Infrared Image Segmentation via Fast Fuzzy C-Means with Spatial Information. In: IEEE International Conference on Robotics and Biomimetics, pp. 742–745 (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Engineering and Information Technology, University of Ulsan, Ulsan, Korea, 680-749

    Hyunh Van Luong & Jong Myon Kim

Authors
  1. Hyunh Van Luong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jong Myon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Mitsubishi Electric Research Laboratories, P.O. Box 02139, Cambridge, MA, USA

    Fatih Porikli

  7. Computer and Information Science and Engineering, University of Florida, P.O. Box, FL 32611-6120, Gainsville, USA

    Jörg Peters

  8. IBM T.J. Watson Research Center, 19 Skyline Drive, NY 10532, Hawthorne, USA

    James Klosowski

  9. 128 Memorial Mall, Stewart B001, IN 47907, West Lafayette, USA

    Laura Arns

  10. Denver Museum of Nature and Space, 2001 Colorade Blvd. Denver,, CO 80205, USA

    Yu Ka Chun

  11. Departmen of Computer Science,, NC State University, Campus Box 8206, NC 27695-8206, Raleigh, USA

    Theresa-Marie Rhyne

  12. Los Alamos National Labs, P.O. Box 1663, NM 87545, Los Alamos, USA

    Laura Monroe

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Van Luong, H., Kim, J.M. (2008). A New Parallel Approach to Fuzzy Clustering for Medical Image Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89639-5_104

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-89639-5_104

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89638-8

  • Online ISBN: 978-3-540-89639-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation