Skip to main content
SpringerLink
Log in

A Multi-scale Scheme for Image Segmentation Using Neuro-fuzzy Classification and Curve Evolution

  • Conference paper
Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3972))

Included in the following conference series:

  • 73 Accesses

Abstract

In this paper, we present a new scheme to segment a given image. This scheme utilizes neuro-fuzzy system to derive a proper set of contour pixels based on multi-scale images. We use these fuzzy derivatives to develop a new curve evolution model. The model automatically detect smooth boundaries, scaling the energy term, and change of topology according to the extracted contour pixels set. We present the numerical implementation and the experimental results based on the semi-implicit method. Experimental results show that one can obtains a high quality edge contour.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Caselles, V., Catte, F., Coll, T., Dibos, F.: A Geometric Model for Active Contours in Image Processing. Nume. Math. 66(3), 1–31 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  2. Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape Modeling with Front Propagation: A Level Set Approach. IEEE Trans. Patt. Analy. and Mach. Intell. 17(2), 158–175 (1995)

    Article  Google Scholar 

  3. Osher, S.J., Sethian, J.A.: Fronts Propagating with Curvature Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations. J. Comp. Phys. 79(1), 12–49 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  4. Mumford, D., Shah, J.: Optimal Approximation by Piecewise Smooth Functions and Associated Variational Problems. Comm. Pure Appl. Math. 42(5), 577–685 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  5. Chan, T.F., Vese, L.A.: Active Contours Without Edges. IEEE Trans. Imag. Proc. 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  6. Kass, M., Witkin, A., Terzopoulos, D.: Snake: Active Contour Models. Int. J. Comp. Visi. 1(4), 321–331 (1988)

    Article  Google Scholar 

  7. Weickert, J., Kuhne, G.: Fast Methods for Implicit Active Contour Models. In: Geometric Level Set Methods in Imaging, Vision and Graphics. Springer, New York (2003)

    Google Scholar 

  8. Weickert, J., Haar Romeny, B.M., Viergever, M.A.: Image Processing Efficient and Reliable Scheme for Nonlinear Diffusion Filtering. IEEE Trans. Imag. Proc. 7(3), 389–410 (1998)

    Article  Google Scholar 

  9. Goldenberg, R., Kimmel, R., Rivlin, E., Rudzsky, M.: Fast Geodesic Active Contours. IEEE Trans. Imag. Proc. 10(10), 1467–1475 (2001)

    Article  MathSciNet  Google Scholar 

  10. Castiello, C., Castellano, G., Caponetti, L., Fanelli, A.M.: Fuzzy Classification of Image Pixels. In: Ruspini, E.H., Várkonyi-Kóczy, A.R. (eds.) Proceedings of the IEEE International Workshop on Intelligent Signal Processing, vol. 1, pp. 79–82. IEEE I&M Society, Budapest (2003)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information and Electronic Engineering, Shandong Institute of Business and Technology, Yantai, 264005, China

    Da Yuan, Hui Fan & Fu-guo Dong

  2. School of Computer Science and Technology, Shandong University, Jinan, 250100, China

    Da Yuan

Authors
  1. Da Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fu-guo Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yuan, D., Fan, H., Dong, Fg. (2006). A Multi-scale Scheme for Image Segmentation Using Neuro-fuzzy Classification and Curve Evolution. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_75

Download citation

  • DOI: https://doi.org/10.1007/11760023_75

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34437-7

  • Online ISBN: 978-3-540-34438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation