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Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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Segmentation and Edge Detection Based on Modified ant Colony Optimization for Iris Image Processing

Abbas Biniaz
  • M.Sc. Student, Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
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/ Ataollah Abbasi
  • Assistant professor, Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
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Published Online: 2014-12-30 | DOI: https://doi.org/10.2478/jaiscr-2014-0010


Ant colony optimization (stocktickerACO) is a meta-heuristic algorithm inspired by food searching behavior of real ants. Recently stocktickerACO has been widely used in digital image processing. When artificial ants move in a discrete habitat like an image, they deposit pheromone in their prior position. Simultaneously, vaporizing of pheromone in each iteration step avoids from falling in the local minima trap. Iris recognition because of its great dependability and non-invasion has various applications. simulation results demonstrate stocktickerACO algorithm can effectively extract the iris texture. Also it is not sensitive to nuisance factors. Moreover, stocktickerACO in this research preserves details of the various synthetic and real images. Performance of ACO in iris segmentation is compared with operation of traditional approaches such as canny, robert, and sobel edge detections. Experimental results reveal high quality and quite promising of stocktickerACO to segment images with irregular and complex structures.


  • [1] P. Khaw, ”Iris recognition technology for improved authentication,” SANS Institute, 2002.Google Scholar

  • [2] L. Masek, ”Recognition of human iris patterns for biometric identification,” M. Thesis, The University of Western Australia, 2003.Google Scholar

  • [3] R. Bremananth and A. Chitra, ”New methodology for a person identification system,” Sadhana, vol. 31, pp. 259-276, 2006.Google Scholar

  • [4] S. Shah and A. Ross, ”Iris segmentation using geodesic active contours,” Information Forensics and Security, IEEE Transactions on, vol. 4, pp. 824-836, 2009.Google Scholar

  • [5] X. Liu, K. W. Bowyer, and P. J. Flynn, ”Experiments with an improved iris segmentation algorithm,” 2005, pp. 118-123.Google Scholar

  • [6] N. Tripathy and U. Pal, ”Handwriting segmentation of unconstrained Oriya text,” Sadhana, vol. 31, pp. 755-769, 2006.Google Scholar

  • [7] D. Cockburn, ”A study of the validity of iris diagnosis,” The Australian Journal of Optometry, vol. 64, pp. 154-157, 1981.Google Scholar

  • [8] L. Ma, K. Wang, and D. Zhang, ”A universal texture segmentation and representation scheme based on ant colony optimization for iris image processing,” Computers & Mathematics with Applications, vol. 57, pp. 1862-1868, 2009.Google Scholar

  • [9] M. A. Balafar, A. R. Ramli, M. I. Saripan, and S. Mashohor, ”Review of brain MRI image segmentation methods,” Artificial Intelligence Review, vol. 33, pp. 261-274, 2010.Web of ScienceGoogle Scholar

  • [10] R. Kasturi, L. O’gorman, and V. Govindaraju, ”Document image analysis: A primer,” Sadhana, vol. 27, pp. 3-22, 2002.Google Scholar

  • [11] W. K. Kong and D. Zhang, ”Detecting eyelash and reflection for accurate iris segmentation,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 17, pp. 1025-1034, 2003.Google Scholar

  • [12] M. J. Aligholizadeh, S. Javadi, R. Sabbaghi- Nadooshan, and K. Kangarloo, ”An Effective Method for Eyelashes Segmentation Using Wavelet Transform,” 2011, pp. 185-188.Google Scholar

  • [13] Y. Chen, S. Dass, and A. Jain, ”Localized iris image quality using 2-D wavelets,” Advances in Biometrics, pp. 373-381, 2005.Google Scholar

  • [14] M. Mahlouji, A. Noruzi, and I. Kashan, ”Human Iris Segmentation for Iris Recognition in Unconstrained Environments,” 2012.Google Scholar

  • [15] V. Ramos and F. Almeida, ”Artificial ant colonies in digital image habitats-a mass behaviour effect study on pattern recognition,” Arxiv preprint cs/0412086, 2004.Google Scholar

  • [16] D. R. Chialvo and M. M. Millonas, ”How swarms build cognitive maps,” NATO ASI SERIES F COMPUTER AND SYSTEMS SCIENCES, vol. 144, pp. 439-439, 1995.Google Scholar

  • [17] T. Niknam, R. Khorshidi, and B. B. Firouzi, ”A hybrid evolutionary algorithm for distribution feeder reconfiguration,” Sadhana, vol. 35, pp. 139-162, 2010.Google Scholar

  • [18] H. Cao, P. Huang, and S. Luo, ”A novel image segmentation algorithm based on artificial ant colonies,” Medical Imaging and Informatics, pp. 63-71, 2008.Google Scholar

  • [19] P. Huang, H. Cao, and S. Luo, ”An artificial ant colonies approach to medical image segmentation,” Computer Methods and Programs in Biomedicine, vol. 92, pp. 267-273, 2008.Web of ScienceGoogle Scholar

  • [20] S. A. Etemad and T. White, ”An ant-inspired algorithm for detection of image edge features,” Applied Soft Computing, vol. 11, pp. 4883-4893, 2011.Web of ScienceGoogle Scholar

About the article

Published Online: 2014-12-30

Published in Print: 2013-04-01

Citation Information: Journal of Artificial Intelligence and Soft Computing Research, Volume 3, Issue 2, Pages 133–141, ISSN (Online) 2083-2567, DOI: https://doi.org/10.2478/jaiscr-2014-0010.

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© 2015. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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