Genetic Snakes for Color Images Segmentation

  • Lucia Ballerini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2037)


The world of meat faces a permanent need for new methods of meat quality evaluation. Recent advances in the area of computer and video processing have created new ways to monitor quality in the food industry. In this paper we propose a segmentation method to separate connective tissue from meat. We propose the use of Genetic Snakes, that are active contour models, also known as snakes, with an energy minimization procedure based on Genetic Algorithms (GA). Genetic Snakes have been proposed to overcome some limits of the classical snakes, as initialization, existence of multiple minima, and the selection of elasticity parameters, and have both successfully applied to medical and radar images. We extend the formulation of Genetic Snakes in two ways, by exploring additional internal and external energy terms and by applying them to color images. We employ a modified version of the image energy which considers the gradient of the three color RGB (red, green and blue) components. Experimental results on synthetic images as well as on meat images are reported. Images used in this work are color camera photographs of beef meat.


Genetic Algorithm Active Contour Radar Image Synthetic Image Active Contour Model 
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.
    G. Monin, “Recent methods for predicting quality of whole meat”, Meat Science, vol. 49,no. Suppl. 1, pp. S231–S243, 1998.CrossRefGoogle Scholar
  2. 2.
    L. Ballerini, A. Högberg, K. Lundström, and G. Borgefors, “Colour image analysis technique for measuring of fat in meat: An application for the meat industry”, To appear in: Proc. Electronic Imaging, 2001.Google Scholar
  3. 3.
    L. Ballerini, “Genetic snakes for medical images segmentation”, Lectures Notes in Computer Science, vol. 1596, pp. 59–73, 1999.Google Scholar
  4. 4.
    M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models”, International Journal of Computer Vision, vol. 1,no.4, pp. 321–331, 1988.CrossRefzbMATHGoogle Scholar
  5. 5.
    D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA, 1989.zbMATHGoogle Scholar
  6. 6.
    T. McInerney and D. Terzopoulos, “Deformable models in medical image analysis: A survey”, Medical Image Analysis, vol. 1,no. 2, pp. 91–108, 1996.CrossRefGoogle Scholar
  7. 7.
    Poli, Voigt, Cagnoni, Corne, Smith, and Fogarty, Eds., Evolutionary Image Analysis, Signal Processing and Telecommunications, vol. 1596 of Lectures Notes in Computer Science, Goteborg, Sweden, 1999. Springer-Verlag.Google Scholar
  8. 8.
    “Special issue on genetic algorithms”, Pattern Recognition Letters, vol. 16,no. 8, 1995.Google Scholar
  9. 9.
    C. Bounsaythip and J. Alander, “Genetic algorithms in image processing-a review”, in Proc. 3nwga (3rd Nordic Workshop on Genetic Algorithms), Helsinki, Finland, 1997.Google Scholar
  10. 10.
    D.N. Chun and H.S. Yang, “Robust image segmentation using genetic algorithm with a fuzzy measure”, Pattern Recognition, vol. 29,no. 7, pp. 1195–1211, 1996.CrossRefGoogle Scholar
  11. 11.
    B. Bhanu, S. Lee, and J. Ming, “Adaptive image segmentation using a genetic algorithm”, IEEE Transactions on Systems, Man and Cybernetics, vol. 25,no. 12, pp. 1543–1567, December 1995.CrossRefGoogle Scholar
  12. 12.
    P. Andrey, “Selectionist relaxation: Genetic algorithms applied to image segmentation”, Image and Vision Computing, vol. 17, pp. 175–187, 1999.CrossRefGoogle Scholar
  13. 13.
    L. Ballerini and E. Piazza, “Genetic snakes for radar images segmentation”, in proc. IEEE International Symposium on Intelligent Signal Processing and Communication Systems, Phucket, Thailand, December 1999, pp. 621–624.Google Scholar
  14. 14.
    L. Ballerini, Computer Aided Diagnosis in Ocular Fundus Images, PhD thesis, Università di Firenze, Italy, 1998.Google Scholar
  15. 15.
    D. Beasley, D.R. Bull, and R.R. Martin, “An overview of genetic algorithms: Part 1, fundamentals”, University Computing, vol. 15,no. 2, pp. 58–69, 1993.Google Scholar
  16. 16.
    D. Beasley, D.R. Bull, and R.R. Martin, “An overview of genetic algorithms: Part 2, research topics”, University Computing, vol. 15,no. 4, pp. 170–181, 1993.Google Scholar
  17. 17.
    L.D. Cohen and I. Cohen, “Finite element methods for active contour models and balloons for 2D and 3D images”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15,no. 11, pp. 1131–1147, November 1993.CrossRefGoogle Scholar
  18. 18.
    L.D. Cohen, “On active contour models and balloons”, Computer Vision, Graphics, and Image Processing: Image Understanding, vol. 53,no. 2, pp. 211–218, March 1991.zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Lucia Ballerini
    • 1
  1. 1.Centre for Image AnalysisSwedish University for Agricultural SciencesUppsalaSweden

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