A New Modification of Fuzzy C-Means via Particle Swarm Optimization for Noisy Image Segmentation

  • Saeed MirghasemiEmail author
  • Ramesh Rayudu
  • Mengjie Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9592)


This paper presents a new clustering-based algorithm for noisy image segmentation. Fuzzy C-Means (FCM), empowered with a new similarity metric, acts as the clustering method. The common Euclidean distance metric in FCM has been modified with information extracted from a local neighboring window surrounding each pixel. Having different local features extracted for each pixel, Particle Swarm Optimization (PSO) is utilized to combine them in a weighting scheme while forming the proposed similarity metric. This allows each feature to contribute to the clustering performance, resulting in more accurate segmentation results in noisy images compared to other state-of-the-art methods.


Particle swarm optimization Fuzzy C-means Noisy image segmentation Clustering-based segmentation Similarity metrics 


  1. 1.
    Zhuang, H., Low, K.-S., Yau, W.-Y.: Multichannel pulse-coupled-neural-network-based color image segmentation for object detection. IEEE Trans. Ind. Electron. 59(8), 3299–3308 (2012)CrossRefGoogle Scholar
  2. 2.
    AntúNez, E., Marfil, R., Bandera, J.P., Bandera, A.: Part-based object detection into a hierarchy of image segmentations combining color and topology. Pattern Recogn. Lett. 34(7), 744–753 (2013)CrossRefGoogle Scholar
  3. 3.
    Ferrari, V., Tuytelaars, T., Van Gool, L.: Simultaneous object recognition and segmentation by image exploration. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds.) Toward Category-Level Object Recognition. LNCS, vol. 4170, pp. 145–169. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Kang, Y., Yamaguchi, K., Naito, T., Ninomiya, Y.: Multiband image segmentation and object recognition for understanding road scenes. IEEE Trans. Intell. Transp. Syst. 12(4), 1423–1433 (2011)CrossRefGoogle Scholar
  5. 5.
    Mei, X., Lang, L.: An image retrieval algorithm based on region segmentation. Appl. Mech. Mater. 596, 337–341 (2014)CrossRefGoogle Scholar
  6. 6.
    Zhang, J.-Y., Zhang, W., Yang, Z.-W., Tian, G.: A novel algorithm for fast compression and reconstruction of infrared thermographic sequence based on image segmentation. Infrared Phys. Technol. 67, 296–305 (2014)CrossRefGoogle Scholar
  7. 7.
    Mahalingam, T., Mahalakshmi, M.: 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, pp. 253–260 (2010)Google Scholar
  8. 8.
    Zhang, Q., Kamata, S., Zhang, J.: Face detection and tracking in color images using color centroids segmentation. In: 2008 IEEE International Conference on Robotics and Biomimetics, ROBIO 2008, pp. 1008–1013 (2009)Google Scholar
  9. 9.
    Unnikrishnan, R., Pantofaru, C., Hebert, M.: Toward objective evaluation of image segmentation algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 929–944 (2007)CrossRefGoogle Scholar
  10. 10.
    Wang, X.-Y., Wang, Q.-Y., Yang, H.-Y., Bu, J.: Color image segmentation using automatic pixel classification with support vector machine. Neurocomputing 74(18), 3898–3911 (2011)CrossRefGoogle Scholar
  11. 11.
    Cai, W., Chen, S., Zhang, D.: Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recogn. 40(3), 825–838 (2007)CrossRefzbMATHGoogle Scholar
  12. 12.
    Mirghasemi, S., Sadoghi Yazdi, H., Lotfizad, M.: A target-based color space for sea target detection. Appl. Intell. 36(4), 960–978 (2012)CrossRefGoogle Scholar
  13. 13.
    Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 3(3), 32–57 (1973)CrossRefMathSciNetzbMATHGoogle Scholar
  14. 14.
    Hathaway, R., Bezdek, J., Hu, Y.: Generalized fuzzy c-means clustering strategies using LP norm distances. IEEE Trans. Fuzzy Syst. 8(5), 576–582 (2000)CrossRefGoogle Scholar
  15. 15.
    Ahmed, M.N., Yamany, S.M., Mohamed, N.A., Farag, A.A.: A modified fuzzy c-means algorithm for MRI bias field estimation and adaptive segmentation. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 72–81. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  16. 16.
    Chen, S., Zhang, D.: Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 34(4), 1907–1916 (2004)CrossRefGoogle Scholar
  17. 17.
    Szilagyi, L., Benyo, Z., Szilagyi, S., Adam, H.: Mr brain image segmentation using an enhanced fuzzy C-means algorithm. In: 2003 Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1, pp. 724–726. September 2003Google Scholar
  18. 18.
    Krinidis, S., Chatzis, V.: A robust fuzzy local information C-means clustering algorithm. IEEE Trans. Image Process. 19(5), 1328–1337 (2010)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Wang, X., Lin, X., Yuan, Z.: An edge sensing fuzzy local information C-means clustering algorithm for image segmentation. In: Huang, D.-S., Jo, K.-H., Wang, L. (eds.) ICIC 2014. LNCS, vol. 8589, pp. 230–240. Springer, Heidelberg (2014)Google Scholar
  20. 20.
    Feng, J., Jiao, L., Zhang, X., Gong, M., Sun, T.: Robust non-local fuzzy C-means algorithm with edge preservation for SAR image segmentation. Signal Process. 93(2), 487–499 (2013)CrossRefGoogle Scholar
  21. 21.
    Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: 1995 Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS 1995, pp. 39–43 (1995)Google Scholar
  22. 22.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Network, vol. 4, pp. 1942–1948 (1995)Google Scholar
  23. 23.
    Engelbrecht, A.P.: Computational Intelligence: An Introduction, 2nd edn. Wiley Publishing, Hoboken (2007)CrossRefGoogle Scholar
  24. 24.
    Benaichouche, A., Oulhadj, H., Siarry, P.: Improved spatial fuzzy C-means clustering for image segmentation using PSO initialization, mahalanobis distance and post-segmentation correction. Digital Signal Process. 23(5), 1390–1400 (2013)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Tran, D.C., Wu, Z., Tran, V.H.: Fast generalized fuzzy C-means using particle swarm optimization for image segmentation. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds.) ICONIP 2014, Part II. LNCS, vol. 8835, pp. 263–270. Springer, Heidelberg (2014)Google Scholar
  26. 26.
    Zhang, Q., Huang, C., Li, C., Yang, L., Wang, W.: Ultrasound image segmentation based on multi-scale fuzzy C-means and particle swarm optimization. In: IET International Conference on Information Science and Control Engineering 2012, ICISCE 2012, pp. 1–5. December 2012Google Scholar
  27. 27.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the 8th International Conference on Computer Vision, vol. 2, pp. 416–423 (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Saeed Mirghasemi
    • 1
    Email author
  • Ramesh Rayudu
    • 1
  • Mengjie Zhang
    • 1
  1. 1.School of Engineering and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand

Personalised recommendations