Image Space Colonization Algorithm

  • Leonardo Bocchi
  • Lucia Ballerini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)


This paper describes an image segmentation method based on an evolutionary approach. Unlike other application of evolutionary algorithms to this problem, our method does not require the definition of a global fitness function. Instead a survival probability for each individual guides the progress of the algorithm. 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 various sets of experiments on simulated MR brain images in order to determine the optimal parameter settings. Experimental results on real image are also reported. Images used in this work are color camera photographs of beef meat.


Image Segmentation Segmentation Result Meat Quality Confusion Matrice Beef Meat 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognition 26, 1277–1294 (1993)CrossRefGoogle Scholar
  2. 2.
    Bhanu, B., Lee, S., Ming, J.: Adaptive image segmentation using a genetic algorithm. IEEE Transactions on Systems, Man and Cybernetics 25, 1543–1567 (1995)CrossRefGoogle Scholar
  3. 3.
    Bhandarkar, S.M., Zhang, H.: Image segmentation using evolutionary computation. IEEE Transactions on Evolutionary Computation 3, 1–21 (1999)CrossRefGoogle Scholar
  4. 4.
    Andrey, P.: Selectionist relaxation: Genetic algorithms applied to image segmentation. Image and Vision Computing 17, 175–187 (1999)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    Veenman, C.J., Reinders, M.J.T., Backer, E.: Acellular coevolutionary algorithmfor image segmentation. IEEE Transactions on Image Processing 12, 304–313 (2003)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Ramos, V., Almeida, F.: Artificial ant colonies in digital image habitats - a mass behaviour effect study on pattern recognition. In: Bosma, W. (ed.) ANTS 2000. LNCS, vol. 1838, pp. 113–116. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  8. 8.
    Gardner, M.: The fantastic combinations of John Conway’s new solitaire game ”life”. Scientifican American 223, 120–123 (1970)CrossRefGoogle Scholar
  9. 9.
    Bocchi, L., Ballerini, L., Hässler, S.: A new evolutionary algorithm for image segmentation. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 264–273. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Collins, D.L., Zijdenbos, A.P., Kollokian, V., Sled, J.G., Kabani, N.J., Holmes, C.J., Evans, A.C.: Design and construction of a realistic digital brain phantom. IEEE Transactions on Medical Imaging 17, 463–468 (1998)CrossRefGoogle Scholar
  11. 11.
    Ballerini, L.: Genetic snakes for color images segmentation. In: Boers, E.J.W., Gottlieb, J., Lanzi, P.L., Smith, R.E., Cagnoni, S., Hart, E., Raidl, G.R., Tijink, H. (eds.) EvoIASP 2001, EvoWorkshops 2001, EvoFlight 2001, EvoSTIM 2001, EvoCOP 2001, and EvoLearn 2001. LNCS, vol. 2037, pp. 268–277. Springer, Heidelberg (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Leonardo Bocchi
    • 1
  • Lucia Ballerini
    • 2
  1. 1.Dept. of Electronics and TelecommunicationsUniversity of FlorenceFirenzeItaly
  2. 2.Dept. of Food ScienceSwedish University of Agricultural SciencesUppsalaSweden

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