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A New Multilevel Thresholding Method Using Swarm Intelligence Algorithm for Image Segmentation

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DOI: 10.4236/jilsa.2010.23016    6,112 Downloads   13,959 Views   Citations

ABSTRACT

Thresholding is a popular image segmentation method that converts gray-level image into binary image. The selection of optimum thresholds has remained a challenge over decades. In order to determine thresholds, most methods analyze the histogram of the image. The optimal thresholds are often found by either minimizing or maximizing an objective function with respect to the values of the thresholds. In this paper, a new intelligence algorithm, particle swarm opti-mization (PSO), is presented for multilevel thresholding in image segmentation. This algorithm is used to maximize the Kapur’s and Otsu’s objective functions. The performance of the PSO has been tested on ten sample images and it is found to be superior as compared with genetic algorithm (GA).

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

S. P. Duraisamy and R. Kayalvizhi, "A New Multilevel Thresholding Method Using Swarm Intelligence Algorithm for Image Segmentation," Journal of Intelligent Learning Systems and Applications, Vol. 2 No. 3, 2010, pp. 126-138. doi: 10.4236/jilsa.2010.23016.

References

[1] J. K. Udupa and S. Samarasekera, “Fuzzy Connectedness and Object Definition: Theory, Algorithms and Applica-tions in Image Segmentation,” Graphical Models and Image Processing, Vol. 58, No. 3, 1996, pp. 246-261.
[2] P. K. Sahoo, S. Soltani and A. K. C. Wong, “A Survey of Thresholding Techniques,” Computer Vision, Graphics and Image Processing, Vol. 41, No. 2, 1998, pp. 233-260.
[3] N. P. Pal and S. K. Pal, “A Review on Image Segmenta-tion Techniques,” Pattern Recognition, Vol. 26, No. 9, 1993, pp. 1277-1294.
[4] M. Sezgin and B. Sankar, “Survey over Image Thre-sholding Techniques and Quantitative Performance Eval-uation,” Journal of Electronic Imaging, Vol. 13, No. 1, 2004, pp. 146-165.
[5] S. U. Lee, S. Y. Chung and R. H. Park, “A Comparative Performance Study of Several Global Thresholding Techniques for Segmentation,” Computer Vision, Graph-ics and Image Processing, Vol. 52, No. 2, 1990, pp. 171-190.
[6] N. Otsu, “A Threshold Selection Method from Gray- Level Histograms,” IEEE Transaction on Systems, Man and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.
[7] W. Tao, H. Jin and L. Liu, “Object Segmentation Using Ant Colony Optimization Algorithm and Fuzzy Entropy,” Pattern Recognition Letters, Vol. 28, No. 7, 2007, pp. 788-796.
[8] O. J. Tobias and R. Seara, “Image Segmentation by His-togram Thresholding Using Fuzzy Sets,” IEEE Transac-tion on Image Processing, Vol. 11, No. 12, 2002, pp. 1457-1465.
[9] J. N. Kapur, P. K. Sahoo and A. K. C. Wong, “A New Method for Gray-Level Picture Thresholding Using the Entropy of The Histogram,” Computer Vision, Graphics and Image Processing, Vol. 29, No. 3, 1985, pp. 273-285.
[10] T. Pun, “Entropy Thresholding: A New Approach,” Computer Vision, Graphics and Image Processing, Vol. 16, No. 3, 1981, pp. 210-239.
[11] A. D. Brink, “Minimum Spatial Entropy Threshold Selec-tion,” IEEE Proceedings, Vision Image and Signal Processing, Vol. 142, No. 3, 1995, pp. 128-132.
[12] X. Li, Z. Zhao and H. D. Cheng, “Fuzzy Entropy Thre-shold Approach to Breast Cancer Detection,” Information Sciences, Vol. 4, No. 1, 1995, pp.49-56.
[13] L. K. Huang and M. J. Wang, “Image Thresholding by Minimizing the Measure of Fuzziness,” Pattern Recogni-tion, Vol. 28, No. 1, 1995, pp. 41-51.
[14] J. Kittler and J. Illingworth, “Minimum Error Threshold-ing,” Pattern Recognition, Vol. 19, No. 1, 1986, pp. 41-47.
[15] Q. Ye and P. Danielsson, “On Minimum Error Thre-sholding and its Implementation,” Pattern Recognition Letters, Vol. 7, No. 4, 1988, pp. 201-206.
[16] U. Gonzales-Baron and F. Butler, “A Comparison of Seven Thresholding Techniques with the K-Means Clus-tering Algorithm for Measurement of Bread-Crumb Fea-tures by Digital Image Analysis,” Journal of Food Engi-neering, Vol. 74, No. 2, 2006, pp. 268-278.
[17] P. Y. Yin and L. H. Chen, “A Fast Iterative Scheme For Multilevel Thresholding Methods,” Signal Processing, Vol. 60, No. 3, 1997, pp. 305-313.
[18] P. S. T. Liao, S. Chen and P. C. Chung, “A Fast Algo-rithm for Multilevel Thresholding,” Journal of Information Science and Engineering, Vol. 17, No. 5, 2001, pp. 713-727.
[19] K. C. Lin, “Fast Image Thresholding by Finding Zero(S) of the First Derivative of between Class Variance,” Ma-chine Vision and Applications, Vol. 13, No. 5-6, 2003, pp. 254-262.
[20] P.-Y. Yin and L.-H. Chen, “A Fast Iterative Scheme for Multilevel Thresholding Methods,” Signal Processing, Vol. 60, No. 3, 1997, pp. 305-313.
[21] E. Cuevas, D. Zaldivar and M. Perez-Cisneros, “A Novel Multi-Threshold Segmentation Approach Based on Dif-ferential Evolution Optimization,” Expert Systems with Applications, Vol. 37, No. 7, 2010, pp. 5265-5271.
[22] W. B. Tao, H. Jin and L. M. Liu, “Object Segmentation Using Ant Colony Optimization Algorithm and Fuzzy Entropy,” Pattern Recognition Letters, Vol. 28, No. 7, 2008, pp. 788-796.
[23] P. Y. Yin, “A Fast Scheme for Optimal Thresholding Using Genetic Algorithms,” Signal Processing, Vol. 72, No. 2, 1999, pp. 85-95.
[24] C. C. Lai and D. C. Tseng, “A Hybrid Approach Using Gaussian Smoothing and Genetic Algorithm for Multilevel Thresholding,” International Journal of Hybrid Intelligent Systems, Vol. 1, No. 3, 2004, pp. 143-152.
[25] D. B. Fogel, “Evolutionary Computation: Toward a New Philosophy of Machine Intelligence,” 2nd Edition, IEEE Press, Piscataway, 2000.
[26] J. Kennedy and R. Eberhart, “Particle Swarm Optimiza-tion,” Proceedings of the IEEE Conference on Neural Networks—ICNN’95, Perth, Vol. 4, 1995, pp. 1942-1948.
[27] Y.-T. Kao, E. Zahara and I-W. Kao, “A Hybridized Ap-proach to Data Clustering,” Expert Systems with Applica-tions, Vol. 34, No. 3, 2008, pp. 1754-1762.
[28] Z.-J. Lee, S.-W. Lin, S.-F. Su and C.-Y. Lin, “A Hybrid Watermarking Technique Applied to Digital Images,” Expert Systems with Applications, Vol. 8, No. 1, 2008, pp. 789-808.
[29] C.-C. Tseng, J.-G. Hsieh and J.-H. Jeng, “Fractal Image Compression Using Visual-Based Particle Swarm Opti-mization,” Image and Vision Computing, Vol. 26, No. 8, 2008, pp. 1154-1162.

  
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