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Opto-Electronics Review

Editor-in-Chief: Jaroszewicz, Leszek

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Volume 16, Issue 2


Finite grade pheromone ant colony optimization for image segmentation

F. Yuanjing / Y. Li / K. Liangjun
Published Online: 2008-03-26 | DOI: https://doi.org/10.2478/s11772-008-0009-0


By combining the decision process of ant colony optimization (ACO) with the multistage decision process of image segmentation based on active contour model (ACM), an algorithm called finite grade ACO (FACO) for image segmentation is proposed. This algorithm classifies pheromone into finite grades and updating of the pheromone is achieved by changing the grades and the updated quantity of pheromone is independent from the objective function. The algorithm that provides a new approach to obtain precise contour is proved to converge to the global optimal solutions linearly by means of finite Markov chains. The segmentation experiments with ultrasound heart image show the effectiveness of the algorithm. Comparing the results for segmentation of left ventricle images shows that the ACO for image segmentation is more effective than the GA approach and the new pheromone updating strategy appears good time performance in optimization process.

Keywords: active contour model; ant colony optimization; image segmentation

  • [1] M. Kass, A. Witkin, and D. TeizoPoulos, “Snakes: active contour models”, Int. J. Comp. Visiton 1, 321–331 (1978). http://dx.doi.org/10.1007/BF00133570CrossrefGoogle Scholar

  • [2] L.D. Cohen, “On active contour models and balloons”, Comp. Vision Graph. 53, 211–218 (1991). Google Scholar

  • [3] A.A. Amini and C.R. Jain, “Using dynamic programming for solving variational problems in vision”, IEEE T. Pattern Anal. 12, 855–867 (1990). http://dx.doi.org/10.1109/34.57681CrossrefGoogle Scholar

  • [4] D.J. Williams and M. Shah, “A fast algorithm for active contours”, Proc. 3rd Int. Conf. on Computer Vision, 592–595 (1990). Google Scholar

  • [5] L.A. MacEachern and T. Manku, “Genetic algorithms for active contour optimization”, Proc. IEEE Int. Symp. on Circuits and Systems 4, 229–232 (1998). Google Scholar

  • [6] S.R. Gunn and M.S. Nixon, “Snake head boundary extraction using global and local energy minimization”, Proc. 13 th Int. Conf. on Pattern Recognition, 581–585 (1996). Google Scholar

  • [7] C. Xu and J.L. Prince, “Snake shape and gradient vector flow”, IEEE T. Pattern Anal. 7, 359–369 (1998). Google Scholar

  • [8] W. Hsien-Hsun, L. Jyh-Charn, and C. Chui, “A wavelet-frame based image force model for active contouring algorithms”, IEEE T. Image Process. 9, 1983–1988 (2000). http://dx.doi.org/10.1109/83.877221CrossrefGoogle Scholar

  • [9] M. Dorigo, V. Manjezzo, and A. Colorni, “The ant system: Optimization by a colony of cooperating agents”, IEEE T. Syst. Man Cy. B2692, 29–41 (1996). http://dx.doi.org/10.1109/3477.484436CrossrefGoogle Scholar

  • [10] M. Dorigo and L.M. Ganbardella, “Ant colony system: A cooperating learning approach to the travelling salesman problem”, IEEE T. Evolut. Comput. 1, 53–66 (1997). http://dx.doi.org/10.1109/4235.585892CrossrefGoogle Scholar

  • [11] B. Bullnheimer, R. Hartl, and C. Strauss, “Applying the ant system to the vehicle routing problem”, Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, Kluwer Academics, 285–296 (1998). Google Scholar

  • [12] T. Stutzle, “An ant approach to the flow shop problem [C]”, Proc. Eur. Cong. on Intelligent Techniques and Soft Computing, Aachen, Germany, 1560–1564 (1998). Google Scholar

  • [13] S. Ouadfel and M. Batouche, “Ant colony system with local search for Markov random field image segmentation [C]”, Proc. Int. Conf. on Image Processing 1, 133–136 (2003). Google Scholar

  • [14] S. Meshoul and M. Batouche, “Ant colony system with external dynamics for point matching and pose estimation [J]”, Pattern Recogn. 3, 823–826 (2002). Google Scholar

  • [15] F. Yuanjing, “Ant colony cooperative optimization and its application in image segmentation”, PhD Dissertation, Xi’an Jiaotong University, China, 2005. Google Scholar

  • [16] C. Blum and M. Dorigo, “The hyper-cube framework for ant colony optimization”, IEEE T. Syst. Man Cy. 34, 1161–1172 (2004). http://dx.doi.org/10.1109/TSMCB.2003.821450CrossrefGoogle Scholar

  • [17] L. Dazhong, Foundation of Variational Methods, National Defence Industry Press, Beijing, 2004. Google Scholar

  • [18] A. Mishraa, P.K. Duttab, and M.K. Ghoshc, “A GA based approach for boundary detection of left ventricle with echocardio graphic image sequences”, Image Vision Comput. 21, 967–976 (2003). http://dx.doi.org/10.1016/S0262-8856(03)00121-5CrossrefGoogle Scholar

About the article

Published Online: 2008-03-26

Published in Print: 2008-06-01

Citation Information: Opto-Electronics Review, Volume 16, Issue 2, Pages 163–171, ISSN (Online) 1896-3757, DOI: https://doi.org/10.2478/s11772-008-0009-0.

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© 2008 SEP, Warsaw. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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