Skip to main content
SpringerLink
Log in

A New Evolutionary Algorithm for Image Segmentation

  • Conference paper
Applications of Evolutionary Computing (EvoWorkshops 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3449))

Included in the following conference series:

Abstract

This paper describes a new evolutionary algorithm for image segmentation. The evolution involves the colonization of a bidimensional world by a number of populations. The individuals, belonging to different populations, compete to occupy all the available space and adapt to the local environmental characteristics of the world. We present experiments with synthetic images, where we show the efficiency of the proposed method and compare it to other segmentation algorithm, and an application to medical images. Reported results indicate that the segmentation of noise images is effectively improved. Moreover, the proposed method can be applied to a wide variety of images.

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. Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognition 26, 1277–1294 (1993)

    Article  Google Scholar 

  2. Jain, A.K.: Cluster analysis. In: Young, T.Y., Fu, K.S. (eds.) Handbook of Pattern Recognition and Image Processing, pp. 33–57. Academic Press, London (1986)

    Google Scholar 

  3. Cannon, R.L., Dave, J.V., Bezdek, J.C.: Efficient implementation of the fuzzy c-means clustering algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-8, 248–255 (1986)

    Article  Google Scholar 

  4. Chi, Z., Yan, H., Pham, T.: Fuzzy algorithms: with application to image processing and pattern recognition. World Scientific, Singapore (1996)

    Google Scholar 

  5. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  6. Bezdek, J.C., Hathaway, R.J.: Optimization of fuzzy clustering criteria using genetic algorithms. In: Proc. 1st IEEE Conf. Evolutionary Computation, pp. 589–599 (1994)

    Google Scholar 

  7. Hall, L.O., Ozyurt, I.B., Bezdek, J.C.: Clustering with a genetically optimized approach. IEEE Transactions on Evolutionary Computation 3, 103–112 (1999)

    Article  Google Scholar 

  8. Ballerini, L., Bocchi, L., Johansson, C.B.: Image segmentation by a genetic fuzzy c-means algorithm using color and spatial information. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 260–269. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  9. Bhanu, B., Lee, S., Ming, J.: Adaptive image segmentation using a genetic algorithm. IEEE Transactions on Systems, Man and Cybernetics 25, 1543–1567 (1995)

    Article  Google Scholar 

  10. Bhandarkar, S.M., Zhang, H.: Image segmentation using evolutionary computation. IEEE Transactions on Evolutionary Computation 3, 1–21 (1999)

    Article  Google Scholar 

  11. Andrey, P.: Selectionist relaxation: Genetic algorithms applied to image segmentation. Image and Vision Computing 17, 175–187 (1999)

    Article  Google Scholar 

  12. Liu, J., Tang, Y.Y.: Adaptive image segmentation with distributed behavior-based agents. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 544–551 (1999)

    Article  Google Scholar 

  13. Veenman, C.J., Reinders, M.J.T., Backer, E.: A cellular coevolutionary algorithm for image segmentation. IEEE Transactions on Image Processing 12, 304–313 (2003)

    Article  MathSciNet  Google Scholar 

  14. Ramos, V., Almeida, F.: Artificial ant colonies in digital image habitats - a mass behaviour effect study on pattern recognition. In: Proc. of ANTS 2000 - 2nd Int. Workshop on Ant Algorithms (From Ant Colonies to Artificial Ants), pp. 113–116 (2000)

    Google Scholar 

  15. Gardner, M.: The fantastic combinations of John Conway’s new solitaire game “life”. Scientifican American 223, 120–123 (1970)

    Article  Google Scholar 

  16. Lim, Y.W., Lee, S.U.: On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern Recognition 23, 1935–1952 (1990)

    Google Scholar 

  17. Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 841–847 (1991)

    Article  Google Scholar 

  18. Hathaway, R.J., Bezdek, J.C.: Optimization of clustering criteria by reformulation. IEEE Transactions on Fuzzy Systems 3, 241–254 (1995)

    Article  Google Scholar 

  19. Stevens, A., Lowe, J.: Human Histology, C.V. Mosby, 2nd edn. (1997)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Electronics and Telecommunications, University of Florence, Via S.Marta 3, 50139, Firenze, Italy

    Leonardo Bocchi

  2. No institute,  

    Lucia Ballerini

  3. Department of Medical Sciences, Uppsala University, Uppsala University Hospital, 751 85, Uppsala

    Signe Hässler

Authors
  1. Leonardo Bocchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lucia Ballerini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Signe Hässler
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Johannes Gutenberg University, Mainz, Germany

    Franz Rothlauf

  2. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  3. Dipartimento di Ingegneria dell’Informazione, Università di Parma,  

    Stefano Cagnoni

  4. School of Mathematical and Computer Science, Heriot-Watt University, EH14 8AS, Edinburgh, UK

    David Wolfe Corne

  5. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Rolf Drechsler

  6. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

  7. CISUC, Department of Informatics Engineering, University of Coimbra, Polo II of the University of Coimbra, 3030, Coimbra, Portugal

    Penousal Machado

  8. ICIS, Radboud University Nijmegen, The Netherlands

    Elena Marchiori

  9. Universidade de A Coruña, CP 15071, A Coruña, Spain

    Juan Romero

  10. School of Computing Sciences, University of East Anglia, UEA Norwich, NR4 7TJ, Norwich, UK

    George D. Smith

  11. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bocchi, L., Ballerini, L., Hässler, S. (2005). A New Evolutionary Algorithm for Image Segmentation. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2005. Lecture Notes in Computer Science, vol 3449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32003-6_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-32003-6_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25396-9

  • Online ISBN: 978-3-540-32003-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation