Image Segmentation Using Ant System-Based Clustering Algorithm

  • Aleksandar Jevtić
  • Joel Quintanilla-Domínguez
  • José Miguel Barrón-Adame
  • Diego Andina
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 87)


Industrial applications of computer vision sometimes require detection of atypical objects that occur as small groups of pixels in digital images. These objects are difficult to single out because they are small and randomly distributed. In this work we propose an image segmentation method using the novel Ant System-based Clustering Algorithm (ASCA). ASCA models the foraging behaviour of ants, which move through the data space searching for high data-density regions, and leave pheromone trails on their path. The pheromone map is used to identify the exact number of clusters, and assign the pixels to these clusters using the pheromone gradient. We applied ASCA to detection of microcalcifications in digital mammograms and compared its performance with state-of-the-art clustering algorithms such as 1D Self-Organizing Map, k-Means, Fuzzy c-Means and Possibilistic Fuzzy c-Means. The main advantage of ASCA is that the number of clusters needs not to be known a priori. The experimental results show that ASCA is more efficient than the other algorithms in detecting small clusters of atypical data.


Image Segmentation Cluster Centre Pheromone Accumulation Pheromone Trail Digital Mammogram 
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.
    Yu, W.-D., Liu, Y.-C.: Hybridization of CBR and numeric soft computing techniques for mining of scarce construction databases. Automat. Constr. 15(1), 33–46 (2006)CrossRefGoogle Scholar
  2. 2.
    Corchado, E., Herrero, A.: Neural visualization of network traffic data for intrusion detection. Appl. Soft Comput. (2010), doi:10.1016/j.asoc.2010.07.002Google Scholar
  3. 3.
    Andina, D., Pham, D.T.: Computational intelligence: For engineering and manufacturing. Springer, New York (2007)zbMATHGoogle Scholar
  4. 4.
    Sedano, J., Curiel, L., Corchado, E., de la Cal, E., Villar, J.R.: A soft computing method for detecting lifetime building thermal insulation failures. Integr. Comput. -Aided E 17(2), 103–115 (2010)Google Scholar
  5. 5.
    Corchado, E., Arroyo, A., Tricio, V.: Soft computing models to identify typical meteorological days. Log. J. IGPL (2010), doi:10.1093/jigpal/jzq035Google Scholar
  6. 6.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: A review. ACM Comput. Surv. 31(3), 264–323 (1999)CrossRefGoogle Scholar
  7. 7.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: Optimization by a colony of cooperating agents. IEEE T. Syst. Man Cyb. - Part B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  8. 8.
    Kotsiantis, S., Pintelas, P.: Recent advances in clustering: A brief survey. WSEAS Transactions on Information Science and Applications 1(1), 73–81 (2004)Google Scholar
  9. 9.
    Handl, J., Meyer, B.: Ant-based and swarm-based clustering. Swarm Intelligence 1(1), 95–113 (2007)CrossRefGoogle Scholar
  10. 10.
    Kohonen, T.: The self-organizing map. Proceedings of the IEEE 78(9), 1464–1480 (1990)CrossRefGoogle Scholar
  11. 11.
    Richardson, A.J., Risien, C., Shillington, F.A.: Using self-organizing maps to identify patterns in satellite imagery. Prog. Oceanogr. 59, 223–239 (2003)Google Scholar
  12. 12.
    Jiang, Y., Zhou, Z.H.: SOM ensemble-based image segmentation. Neural Process. Lett. 20, 171–178 (2004)CrossRefGoogle Scholar
  13. 13.
    MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proc 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297. University of California Press, Berkeley (1967)Google Scholar
  14. 14.
    Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms, 1st edn. Plenum Press, New York (1981)zbMATHGoogle Scholar
  15. 15.
    Krishnapuram, R., Keller, J.M.: A possibilistic approach to clustering. IEEE T. Fuzzy Syst. 1(2), 98–110 (1993)CrossRefGoogle Scholar
  16. 16.
    Pal, N.R., Pal, K., Bezdek, J.C.: A mixed c-means clustering model. In: Proc 6th IEEE Int Conf. Fuzzy Syst., pp. 11–21 (1997)Google Scholar
  17. 17.
    Pal, N.R., Pal, K., Keller, J.M., Bezdek, J.C.: A possibilitic fuzzy c-means clustering algorithm. IEEE T. Fuzzy Syst. 13(4), 517–530 (2005)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-Means clustering algorithm: Analysis and implementation. IEEE T. Pattern Anal. 24(7), 881–892 (2002)CrossRefGoogle Scholar
  19. 19.
    Laia, J.Z.C., Liaw, Y.C.: Improvement of the k-means clustering filtering algorithm. Pattern Recogn. 41, 3677–3681 (2008)CrossRefGoogle Scholar
  20. 20.
    Chang, K.C., Yeh, M.F.: Grey relational analysis based approach for data clustering. IEE P-Vis. Image Sign. 152(2), 165–172 (2005)CrossRefGoogle Scholar
  21. 21.
    Awad, M., Chehdi, K., Nasri, A.: Multi-component image segmentation using a hybrid dynamic genetic algorithm and fuzzy C-means. IET Image Process. 3(2), 52–62 (2009)CrossRefGoogle Scholar
  22. 22.
    Ojeda-Magaña, B., Quintanilla-Domínguez, J., Ruelas, R., Andina, D.: Images sub-segmentation with the PFCM clustering algorithm. In: Proc 7th IEEE Int Conf. Industrial Informatics, pp. 499–503 (2009)Google Scholar
  23. 23.
    Russell, S.J., Norvig, P.: Artificial intelligence: A modern approach, 2nd edn. Prentice Hall, Upper Saddle River (2003)Google Scholar
  24. 24.
    Barrón-Adame, J.M., Herrera-Delgado, J.A., Cortina-Januchs, M.G., Andina, D., Vega-Corona, A.: Air pollutant level estimation applying a self-organizing neural network. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2007. LNCS, vol. 4528, pp. 599–607. Springer, Heidelberg (2007)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Aleksandar Jevtić
    • 1
  • Joel Quintanilla-Domínguez
    • 1
  • José Miguel Barrón-Adame
    • 2
  • Diego Andina
    • 1
  1. 1.E.T.S.I. de TelecomunicaciónUniversidad Politécnica de MadridMadridSpain
  2. 2.División de Ingenierias, Campus Irapuato-SalamancaUniversidad de GuanajuatoSalamanca, Gto.Mexico

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