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Genetic FCMS Clustering Algorithm for Image Segmentation

  • Chunyu Zhang
  • Pengfei Wang
  • Cuiyin Liu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 113)

Abstract

FCM is populated in image segmentation for its simplicity and easily realization. The classic FCM segmentation used only the gray value for segmentation, and is liable to stuck at local values, and the result is relied on cluster center of initial selection. In this paper, we present a Genetic fuzzy c-means (GFCMS) algorithm that incorporates spatial information for segmentation. The first improvement is to use the spatial information of pixel in FCM algorithm. The second improvement is to use the genetic algorithm for searching the global optimum. The results of the experiment validates that the algorithm has better adaptability and gets the correct global optimum.

Keywords

Fuzzy k-means Genetic algorithm Spatial information Clustering 

References

  1. 1.
    Dunn JC (1973) A fuzzy relative of the ISODTA TA process and its use in detecting compact well separated clusters [J]. J Cybern 3(3):32–57Google Scholar
  2. 2.
    Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms [M]. Plenum Press, New YorkGoogle Scholar
  3. 3.
    Chuang K-S, Tzeng H-L (2006) Fuzzy c-means clustering with spatial information for image segmentation[J]. Comput Med Imaging Graph 30:9–15CrossRefGoogle Scholar
  4. 4.
    Maulik U (2009) Medical image segmentation using genetic algorithms [C]. IEEE Trans Inform Technol Biomed 13(2):166–173Google Scholar
  5. 5.
    Pham DL, Prince JL (1999) An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities [J]. Pattern Recogn Lett 20:57–68CrossRefMATHGoogle Scholar
  6. 6.
    Maulik U, Bandyopadhyay S (1999) Genetic algorithm-based clustering technique [J]. Pattern Recogn 33(2000):1455–1465Google Scholar
  7. 7.
    Andrey P (1997) Selectionist relaxation: genetic algorithms applied to image segmentation [J]. Imag Vis Comput 17(1999):175–187Google Scholar
  8. 8.
    Scheunders P (1997) A genetic c-means clustering algorithm applied to color image quantization [J]. Pattern Recogn 30(6):859–866CrossRefGoogle Scholar
  9. 9.
    Kanungo T, Netanyahu NS, Wu AY (2002) An efficient K-means clustering algorithm: analysis and implementation [J]. IEEE Trans Pattern Anal Mach Intell 7(24):881–893Google Scholar
  10. 10.
    Bezdek JC, Boggavarapu S (1994) Genetic algorithm guided clustering [C]. In: Proceedings of the first IEEE conference on evolutionary computation, pp 34–39Google Scholar
  11. 11.
    Krishna K, Narasimha Murty M (1999) Genetic K-means algorithm [J]. IEEE Trans Syst Man Cybernet Part B 29(3):433–450Google Scholar
  12. 12.
    Coleman GB, Andrews HC (1979) Image segmentation by clustering [J]. Proc IEEE 67:773–791CrossRefGoogle Scholar
  13. 13.
    Clausi DA (2002) K-means iterative fisher (KIF) unsupervised clustering algorithm applied to image texture segmentation [J]. Pattern Recogn 35(2002):1959–1972CrossRefMATHGoogle Scholar
  14. 14.
    Likas A, Vlassis N, Verbeek JJ (2002) The global k-means clustering algorithm [J]. Pattern Recogn Soc 36(2003):451–461Google Scholar
  15. 15.
    Singh M, Patel P, Khosla D, Kim T (1996) Segmentation of functional MRI by K-means clustering. IEEE Trans Nucl Sci 3(3):2030Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.College of Computer ScienceSichuan Panzhihua UniversityPanzhihuaChina
  2. 2.International Baccalaureate Precollege Program 2009The High School Affiliated to Nanjing Normal UniversityNanjingChina

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