Adaptive Initialization of Cluster Centers using Ant Colony Optimization: Application to Medical Images

B. S. Harish, S. V. Aruna Kumar, Francesco Masulli, Stefano Rovetta

2017

Abstract

Segmentation is a fundamental preprocessing step in medical imaging for diagnosis and surgical operations planning. The popular Fuzzy C-Means clustering algorithm perform well in the absence of noise, but it is non robust to noise as it makes use of the Euclidean distance and does not exploit the spatial information of the image. These limitations can be addressed by using the Robust Spatial Kernel FCM (RSKFCM) algorithm that takes advantage of the spatial information and uses a Gaussian kernel function to calculate the distance between the center and data points. Though RSKFCM gives a good result, the main drawback of this method is the inability of obtaining good minima for the objective function as it happens for many other clustering algorithms. To improve the efficiency of RSKFCM method, in this paper, we proposed the Ant Colony Optimization algorithm based RSKFCM (ACORSKFCM). By using the Ant Colony Optimization, RSKFCM initializes the cluster centers and reaches good minima of the objective function. Experimental results carried out on the standard medical datasets like Brain, Lungs, Liver and Breast images. The results show that the proposed approach outperforms many other FCM variants.

References

  1. Ahmed, M. N., Yamany, S. M., Mohamed, N., Farag, A. A., and Moriarty, T. (2002). A modified fuzzy c-means algorithm for bias field estimation and segmentation of mri data. Medical Imaging, IEEE Transactions on, 21(3):193-199.
  2. Archip, N., Rohling, R., Cooperberg, P., Tahmasebpour, H., and Warfield, S. K. (2005). Spectral clustering algorithms for ultrasound image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 862-869.
  3. Arthur, D. and Vassilvitskii, S. (2007). k-means++: The advantages of careful seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pages 1027-1035. Society for Industrial and Applied Mathematics.
  4. Aruna Kumar, S. V. and Harish, B. S. (2014). Segmenting mri brain images using novel robust spatial kernel fcm (rskfcm). Eighth International Conference on Image and Signal Processing, pages 38-44.
  5. Aruna Kumar, S. V. and Harish, B. S. (2015). Segmenting medical images using computational intelligence technique. International Journal of Information Processing, 9(1):48-56.
  6. Aruna Kumar, S. V., Harish, B. S., and Guru, D. S. (2015). Segmenting mri brain images using evolutionary computation technique. In Cognitive Computing and Information Processing (CCIP), International Conference on, pages 1-6.
  7. Bezdek, J. C. (2013). Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media.
  8. Chen, C. W., Luo, J., and Parker, K. J. (1998). Image segmentation via adaptive k-mean clustering and knowledge-based morphological operations with biomedical applications. IEEE Transactions on Image Processing, 7(12):1673-1683.
  9. 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.
  10. Chuang, K.-S., Tzeng, H.-L., Chen, S., Wu, J., and Chen, T.-J. (2006). Fuzzy c-means clustering with spatial information for image segmentation. computerized medical imaging and graphics, 30(1):9-15.
  11. Dorigo, M., Maniezzo, V., and Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 26(1):29-41.
  12. Fukuyama, Y. and Sugeno, M. (1989). A new method of choosing the number of clusters for fuzzy c-means method. In Proceedings of Fifth Fuzzy Systems Symp, pages 247-250.
  13. Hadjahmadi, A. H., Homayounpour, M. M., and Ahadi, S. M. (2008). Robust weighted fuzzy c-means clustering. In IEEE International Conference on Fuzzy Systems(IEEE World Congress on Computational Intelligence), pages 305-311. IEEE.
  14. Han, Y. and Shi, P. (2007). An improved ant colony algorithm for fuzzy clustering in image segmentation. Neurocomputing, 70(4):665-671.
  15. Jain, A. K., Murty, M. N., and Flynn, P. J. (1999). Data clustering: a review. ACM computing surveys (CSUR), 31(3):264-323.
  16. Kuo, C.-T., Walker, P. B., Carmichael, O., and Davidson, I. (2014). Spectral clustering for medical imaging. In 2014 IEEE International Conference on Data Mining, pages 887-892. IEEE.
  17. Ng, H., Ong, S., Foong, K., Goh, P., and Nowinski, W. (2006). Medical image segmentation using k-means clustering and improved watershed algorithm. In IEEE Southwest Symposium on Image Analysis and Interpretation, pages 61-65. IEEE.
  18. Pal, N. R. and Bezdek, J. C. (1995). On cluster validity for the fuzzy c-means model. IEEE Transactions on Fuzzy systems, 3(3):370-379.
  19. Van Lung, H. and Kim, J.-M. (2009). A generalized spatial fuzzy c-means algorithm for medical image segmentation. In Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on, pages 409-414. IEEE.
  20. Wang, W., Zhang, Y., Li, Y., and Zhang, X. (2006). The global fuzzy c-means clustering algorithm. In The Sixth World Congress on Intelligent Control and Automation, volume 1, pages 3604-3607. IEEE.
  21. Xie, X. L. and Beni, G. (1991). A validity measure for fuzzy clustering. IEEE Transactions on pattern analysis and machine intelligence, 13(8):841-847.
  22. Yu, J., Lee, S.-H., and Jeon, M. (2012). An adaptive acobased fuzzy clustering algorithm for noisy image segmentation. International Journal of Innovative Computing Information and Control, 8(6):3907-3918.
  23. Zhang, J., Zhang, X., and Zhang, J. (2011). Image segmentation method based on improved genetic algorithm and fuzzy clustering. Advanced Materials Research, 143:379-383.
Download


Paper Citation


in Harvard Style

S. Harish B., V. Aruna Kumar S., Masulli F. and Rovetta S. (2017). Adaptive Initialization of Cluster Centers using Ant Colony Optimization: Application to Medical Images . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 591-598. DOI: 10.5220/0006210905910598


in Bibtex Style

@conference{icpram17,
author={B. S. Harish and S. V. Aruna Kumar and Francesco Masulli and Stefano Rovetta},
title={Adaptive Initialization of Cluster Centers using Ant Colony Optimization: Application to Medical Images},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={591-598},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006210905910598},
isbn={978-989-758-222-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Adaptive Initialization of Cluster Centers using Ant Colony Optimization: Application to Medical Images
SN - 978-989-758-222-6
AU - S. Harish B.
AU - V. Aruna Kumar S.
AU - Masulli F.
AU - Rovetta S.
PY - 2017
SP - 591
EP - 598
DO - 10.5220/0006210905910598