Fuzzy C-Means Techniques for Medical Image Segmentation

1. Ahmed, M., Yamany, S., Mohamed, N., Farag, A., Moriaty, T.: A modified fuzzy c-means algorithm for bias field estimation and segmentation of mri data. IEEE Trans. Medical Imaging 21, 193–199 (2002)

   Article  Google Scholar 

2. Bezdek, J.: A convergence theorem for the fuzzy isodata clustering algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence 2, 1–8 (1980)

   Article  MATH  Google Scholar 

3. Bradley, P., Fayyad, U.: Refining initial points for k-means clustering. In: 15th Int. Conference on Machine Learning, pp. 91–99 (1998)

   Google Scholar 

4. Cai, W., Chen, S., Zhang, D.: Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognition 40(3), 825–838 (2007)

   Article  MATH  Google Scholar 

5. Chen, S.C., Zhang, D.Q.: Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans. Systems, Man and Cybernetics - Part B: Cybernetics 34, 1907–1916 (2004)

   Article  Google Scholar 

6. Cheng, T., Goldgof, D., Hall, L.: Fast fuzzy clustering. Fuzzy Sets and Systems 93, 49–56 (1998)

   Article  MATH  Google Scholar 

7. Chuang, K., Tzeng, S., Chen, H., Wu, J., Chen, T.: Fuzzy c-means clustering with spatial information for image segmentation. Computerized Medical Imaging and Graphics 30, 9–15 (2006)

   Article  Google Scholar 

8. Comaniciu, D., Meer, P.: Mean shift analysis and applications. In: 7th Int. Conference on Computer Vision, pp. 1197–1203 (1999)

   Google Scholar 

9. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Analysis and Machine Intelligence 24, 603–619 (2002)

   Article  Google Scholar 

10. Dhillon, I., Guan, Y., Kogan, J.: Refining clusters in high dimensional text data. In: 2nd SIAM ICDM Workshop on clustering high dimensional data (2002)

    Google Scholar 

11. Eschrich, S., Ke, J., Hall, L., Goldgof, D.: Fast accurate fuzzy clustering through data reduction. IEEE Trans. Fuzzy Systems 11, 262–270 (2003)

    Article  Google Scholar 

12. Haralick, R.M., Shapiro, L.G.: Image segmentation techniques. Computer Vision, Graphics, and Image Processing 29(1), 100–132 (1985)

    Article  Google Scholar 

13. Hartigan, J.: Clustering algorithms. John Wiley & Sons, New York (1975)

    MATH  Google Scholar 

14. Hu, R., Hathaway, L.: On efficiency of optimization in fuzzy c-means. Neural, Parallel and Scientific Computation 10, 141–156 (2002)

    MATH  MathSciNet  Google Scholar 

15. Kass, M., Witkin, A.P., Terzopoulos, D.: Snakes: Active contour models. Int. Journal of Computer Vision 1(4), 321–331 (1988)

    Article  Google Scholar 

16. Kolen, J., Hutcheson, T.: Reducing the time complexity of the fuzzy c-means algorithm. IEEE Trans. Fuzzy Systems 10(2), 263–267 (2002)

    Article  Google Scholar 

17. Leski, J.: Toward a robust fuzzy clustering. Fuzzy Sets and Systems 137, 215–233 (2003)

    Article  MATH  MathSciNet  Google Scholar 

18. MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: 5th Berkeley Symposium on Mathematical statistics and probability, vol. 1, pp. 281–297 (1967)

    Google Scholar 

19. Mao, J., Jain, A.K.: A self-organising network for hyperellipsoidal clustering (hec). IEEE Trans. Neural Networks 7(1), 16–29 (1996)

    Article  Google Scholar 

20. Szilagyi, L., Benyo, Z., Szilagyii, S.M., Adam, H.S.: MR brain image segmentation using an enhanced fuzzy c-means algorithm. In: 25th IEEE Int. Conference on Engineering in Medicine and Biology, vol. 1, pp. 724–726 (2003)

    Google Scholar 

21. Wang, J., Thiesson, B., Xu, Y.-Q., Cohen, M.: Image and video segmentation by anisotropic kernel mean shift. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3022, pp. 238–249. Springer, Heidelberg (2004)

    Google Scholar 

22. Zhang, B.: Generalized k-harmonic means dynamic weighting of data in unsupervised learning. In: 1st SIAM Int. Conference on Data Mining (2001)

    Google Scholar 

23. Zhou, H., Schaefer, G., Shi, C.: A mean shift based fuzzy c-means algorithm for image segmentation. In: 30th IEEE Int. Conference Engineering in Medicine and Biology (2008)

    Google Scholar 

Download references