A Novel Approach for Fuzzy Connected Image Segmentation

  • Xiaona Zhang
  • Yunjie Zhang
  • Weina Wang
  • Yi Li
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 40)

Abstract

Image segmentation is the problem of finding the homogeneous regions (segments) in an image. The fuzzy connectedness has been applied in image segmentation, and segmented the object accurately. In recent years, how to find the reference seeds automatically for multiple objects image segmentation and speed up the process of large images segmentation as important issues to us. In this work we present a novel TABU search-based approach to choose the reference seeds adaptively and use a vertex set expanding method for fuzzy object extraction in image segmentation. This proposed algorithm would be more practical and with a lower computational complexity than others. The results obtained on real image confirm the validity of the proposed approach.

Keywords

fuzzy connectedness image segmentation vertex set expanding TABU search 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bloch, I.: Fuzzy Connectivity and Mathematical Morphology. Pattern Recognit. Lett. 14, 483–488 (1993)MATHCrossRefGoogle Scholar
  2. 2.
    Udupa, J.K., Samarasekera, S.: Fuzzy connectedness and object definition: Theory, algorithms, and applications in image segmentation. In: Graphical Models Image Process, pp. 246–261 (1996)Google Scholar
  3. 3.
    Saha, P.K., Udupa, J.K., Odhner, D.: Scale-Based fuzzy connected image segmentation: Theory, Algorithms, and Validation. Compute Vision and Image Understanding 77, 145–147 (2000)CrossRefGoogle Scholar
  4. 4.
    Zhuge, Y., Udupa, J.K., Saha, P.K.: Vectorical scale-based fuzzy connected image segmentation. Medical Imaging 4684 (2002)Google Scholar
  5. 5.
    Zhou, Y., Bai, J.: Organ Segmentation Using Atlas Registration and Fuzzy Connectedness. In: Engineering in Medicine and Biology 27th Conference (2005)Google Scholar
  6. 6.
    Udupa, J.K., Samarasekera, S.: Relative fuzzy connectedness and object definition: theory, algorithm, and applications in image segmentation. In: Pattern Analysis and Machine Intelligence (2002)Google Scholar
  7. 7.
    Saha, P.K., Udupa, J.K.: Relative Fuzzy Connectedness among Multiple Objects: Theory, Algorithms, and Applications in image segmentation. In: Computer vision an image understanding (2001)Google Scholar
  8. 8.
    Xie, J., Xing, W.: Modern Optimization Computation. China Tsinghua university press, pp. 53–62 (1999)Google Scholar
  9. 9.
    Kaufmann, A.: Introduction to the Theory of Fuzzy Subsets, vol. 1. Academic Press, New York (1975)Google Scholar
  10. 10.
    Rosenfeld, A.: Fuzzy digital topology. Information Control 40, 76–87 (1992)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Rosenfeld, A.: The fuzzy geometry of image subsets. Pattern Recognit. Lett. 2, 311–317 (1991)CrossRefGoogle Scholar
  12. 12.
    Dellepiane, S., Fontana, F.: Extraction of intensity connectedness for image processing. Pattern Recognit. Lett. 16, 313–324 (1995)CrossRefGoogle Scholar
  13. 13.
    Yin, J., Wu, K.: Graph Theory and Its Algorithm. China University of Technology Press, pp. 1-26, 91-98 (2004)Google Scholar
  14. 14.
    Sipser, M.: Introduction to the Theory of Computation. China Machine Press (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Xiaona Zhang
    • 1
  • Yunjie Zhang
    • 1
  • Weina Wang
    • 2
  • Yi Li
    • 3
  1. 1.Department of Mathematics, Dalian Maritime University, Dalian 116026P.R. China
  2. 2.Jinlin Institute of Chemical Technology, Jinlin 132022P.R. China
  3. 3.Department of Computer, Information Engineering College, Heilongjiang Institute of Science and Technology, Harbin 150027P.R. China

Personalised recommendations