A Heuristic Solution for Noisy Image Segmentation using Particle Swarm Optimization and Fuzzy Clustering

Saeed Mirghasemi, Ramesh Rayudu, Mengjie Zhang

2015

Abstract

Introducing methods that can work out the problem of noisy image segmentation is necessary for real-world vision problems. This paper proposes a new computational algorithm for segmentation of gray images contaminated with impulse noise. We have used Fuzzy C-Means (FCM) in fusion with Particle Swarm Optimization (PSO) to define a new similarity metric based on combining different intensity-based neighborhood features. PSO as a computational search algorithm, looks for an optimum similarity metric, and FCM as a clustering technique, helps to verify the similarity metric goodness. The proposed method has no parameters to tune, and works adaptively to eliminate impulsive noise. We have tested our algorithm on different synthetic and real images, and provided quantitative evaluation to measure effectiveness. The results show that, the method has promising performance in comparison with other existing methods in cases where images have been corrupted with a high density noise.

References

  1. Ahmed, M., Yamany, S., Mohamed, N., and Farag, A. (1999). A Modified Fuzzy C-Means Algorithm for MRI Bias Field Estimation and Adaptive Segmentation. In Taylor, C. and Colchester, A., editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI'99, volume 1679 of Lecture Notes in Computer Science, pages 72-81. Springer Berlin Heidelberg.
  2. AntĂșNez, E., Marfil, R., Bandera, J. P., and Bandera, A. (2013). Part-based object detection into a hierarchy of image segmentations combining color and topology. Pattern Recogn. Lett., 34(7):744-753.
  3. Benaichouche, A., Oulhadj, H., and Siarry, P. (2013). Improved spatial fuzzy c-means clustering for image segmentation using {PSO} initialization, mahalanobis distance and post-segmentation correction. Digital Signal Processing, 23(5):1390 - 1400.
  4. Bovik, A. C. (2005). Handbook of Image and Video Processing (Communications, Networking and Multimedia). Academic Press, Inc., Orlando, FL, USA.
  5. Cai, W., Chen, S., and Zhang, D. (2007). Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognition, 40(3):825-838.
  6. Chen, S. and Zhang, D. (2004). Robust image segmentation using fcm with spatial constraints based on new kernel-induced distance measure. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 34(4):1907-1916.
  7. Dunn, J. C. (1973). A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics, 3(3):32-57.
  8. Eberhart, R. and Kennedy, J. (1995). A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS 7895., Proceedings of the Sixth International Symposium on.
  9. Engelbrecht, A. P. (2007). Computational Intelligence: An Introduction. Wiley Publishing, 2nd edition.
  10. Ferrari, V., Tuytelaars, T., and Van Gool, L. (2006). Simultaneous Object Recognition and Segmentation by Image Exploration. In Ponce, J., Hebert, M., Schmid, C., and Zisserman, A., editors, Toward Category-Level Object Recognition, volume 4170 of Lecture Notes in Computer Science, pages 145-169. Springer Berlin Heidelberg.
  11. Hathaway, R., Bezdek, J., and Hu, Y. (2000). Generalized fuzzy c-means clustering strategies using lp norm distances. Fuzzy Systems, IEEE Transactions on, 8(5):576-582.
  12. Kang, Y., Yamaguchi, K., Naito, T., and Ninomiya, Y. (2011). Multiband image segmentation and object recognition for understanding road scenes. Intelligent Transportation Systems, IEEE Transactions on, 12(4):1423-1433.
  13. Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. In Neural Networks, 1995. Proceedings., IEEE International Conference on, volume 4, pages 1942-1948 vol.4.
  14. Krinidis, S. and Chatzis, V. (2010). A robust fuzzy local information c-means clustering algorithm. Image Processing, IEEE Transactions on, 19(5):1328-1337.
  15. Lim, J. S. (1990). Two-dimensional Signal and Image Processing. Prentice-Hall, Inc., Upper Saddle River, NJ, USA.
  16. Mahalingam, T. and Mahalakshmi, M. (2010). Vision based moving object tracking through enhanced color image segmentation using haar classifiers. In Proceedings of the 2nd International Conference on Trendz in Information Sciences and Computing, TISC-2010, pages 253-260.
  17. Martin, D., Fowlkes, C., Tal, D., and Malik, J. (2001). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proc. 8th Int'l Conf. Computer Vision, volume 2, pages 416-423.
  18. Mei, X. and Lang, L. (2014). An image retrieval algorithm based on region segmentation. Applied Mechanics and Materials, 596:337341. cited By 0.
  19. Mirghasemi, S., Sadoghi Yazdi, H., and Lotfizad, M. (2012). A target-based color space for sea target detection. Applied Intelligence, 36(4):960-978.
  20. Szilagyi, L., Benyo, Z., Szilagyi, S., and Adam, H. (2003). Mr brain image segmentation using an enhanced fuzzy c-means algorithm. In Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE, volume 1, pages 724-726 Vol.1.
  21. Tian, X., Jiao, L., and Zhang, X. (2013). A clustering algorithm with optimized multiscale spatial texture information: application to SAR image segmentation. International Journal of Remote Sensing, 34(4):1111- 1126.
  22. Tran, D., Wu, Z., and Tran, V. (2014). Fast Generalized Fuzzy C-means Using Particle Swarm Optimization for Image Segmentation. In Loo, C., Yap, K., Wong, K., Teoh, A., and Huang, K., editors, Neural Information Processing, volume 8835 of Lecture Notes in Computer Science, pages 263-270. Springer International Publishing.
  23. Wiener, N. (1964). Extrapolation, Interpolation, and Smoothing of Stationary Time Series. The MIT Press.
  24. Zhang, J.-Y., Zhang, W., Yang, Z.-W., and Tian, G. (2014). A novel algorithm for fast compression and reconstruction of infrared thermographic sequence based on image segmentation. Infrared Physics & Technology, 67(0):296-305.
  25. Zhang, Q., Huang, C., Li, C., Yang, L., and Wang, W. (2012). Ultrasound image segmentation based on multi-scale fuzzy c-means and particle swarm optimization. In Information Science and Control Engineering 2012 (ICISCE 2012), IET International Conference on, pages 1-5.
  26. Zhang, Q., Kamata, S., and Zhang, J. (2009). Face detection and tracking in color images using color centroids segmentation. In Robotics and Biomimetics, 2008. ROBIO 2008. IEEE International Conference on, pages 1008-1013.
  27. Zhuang, H., Low, K.-S., and Yau, W.-Y. (2012). Multichannel pulse-coupled-neural-network-based color image segmentation for object detection. Industrial Electronics, IEEE Transactions on, 59(8):3299-3308.
Download


Paper Citation


in Harvard Style

Mirghasemi S., Rayudu R. and Zhang M. (2015). A Heuristic Solution for Noisy Image Segmentation using Particle Swarm Optimization and Fuzzy Clustering . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 17-27. DOI: 10.5220/0005584500170027


in Bibtex Style

@conference{ecta15,
author={Saeed Mirghasemi and Ramesh Rayudu and Mengjie Zhang},
title={A Heuristic Solution for Noisy Image Segmentation using Particle Swarm Optimization and Fuzzy Clustering},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},
year={2015},
pages={17-27},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005584500170027},
isbn={978-989-758-157-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - A Heuristic Solution for Noisy Image Segmentation using Particle Swarm Optimization and Fuzzy Clustering
SN - 978-989-758-157-1
AU - Mirghasemi S.
AU - Rayudu R.
AU - Zhang M.
PY - 2015
SP - 17
EP - 27
DO - 10.5220/0005584500170027