Compare different spatial based Fuzzy-C-Mean (FCM) extensions for MRI image segmentation

2010 The 2nd International Conference on Computer and Automation Engineering, ICCAE 2010

Volumn 5, Issue , 2010, Pages 609-611

  Balafar, M A ^a   Ramli, A R ^a   Mashohor, S ^a   Farzan, A ^a  

^a UNIVERSITY PUTRA MALAYSIA   (Malaysia)

Author keywords

Brain segmentation; FCM

Indexed keywords

BRAIN MRI; BRAIN SEGMENTATION; CLUSTERING PROCESS; MRI IMAGE; NEIGHBOURHOOD; SIMILARITY INDICES; SPATIAL INFORMATIONS;

CLUSTERING ALGORITHMS;

IMAGE SEGMENTATION;

EID: 77952621669     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCAE.2010.5451302     Document Type: Conference Paper

References (27)

1. Ping-Lin Chang. al. el, "Exploiting the Self-Organizing Map for Medical Image Segmentation", CBMS, pp. 281-288, Jun 2007.
2. Yan Zhang, Bogdan J. Matuszewski, Lik-Kwan Shark,"A Novel Medical Image Segmentation Method using Dynamic Programming", MediViz, pp. 69-74, 2007. (Pubitemid 47592276)
3. L. O. Hall, A. M. Bensaid, L. P. Clarke, R. P. Velthuizen, M. S. Silbiger, and J. C. Bezdek, "A comparison of neural network and fuzzy Clustering techniques in segmenting magnetic resonance images of the brain," IEEE Trans. Neural Netw., vol. 3, no. 5, pp. 672-682, Sep. 1992.
4. Tian, Dan., Fan, Linan. : A Brain MR Images Segmentation Method Based on SOM Neural Network. ICBBE. (2007) 686-689.
5. H. Suzuki and J. Toriwaki, "Automatic segmentation of head MRI images by knowledge guided thresholding," Comput. Med. Imag. Graph., vol. 15, no. 4, pp. 233-240, 1991.
6. A. R. Robb, "Biomedical Imaging, Visualization, and Analysis". NewYork: Wiley, 2000.
7. Pohle, R., Toennies, L.D.: Segmentation of Medical Images using Adaptive Region Growing . Proc,SPIE, Medical Imaging, vol. 4322. (2001)
8. Shen, S., Sandham, W., Granat, M., Sterr, A.: MRI Fuzzy Segmentation of Brain Tissue Using Neighbourhood Attraction with Neural-Network Optimization. IEEE Trans. Inform. Tech. Biomedicine. Vol. 9, no. 3. (2005) 459-467
9. Hall, L. O., Bensaid, A. M., Clarke, L. P., Velthuizen, R. P., Silbiger, M. S., Bezdek, J. C.: A Comparison of Neural Network and Fuzzy Clustering Techniques in Segmenting Magnetic Resonance Images of The Brain. IEEE Trans. Neural Netw., vol. 3, no. 5. (1992) 672-682
10. Acton, S. T., Mukherjee, D. P.: Scale Space Classification Using Area Morphology. IEEE Trans. Image Process., vol. 9, no. 4. (2000) 623-635
11. Zhang, D. Q., Chen, S. C.: A Novel Kernelized Fuzzy C-means Algorithm With Application in Medical Image Segmentation. Artif. Intell.Med., vol. 32. (2004) 37-52
12. Dave, R. N. R. N.: Characterization and Detection of Noise in Clustering. Pattern Recognit. Lett., vol. 12. (1991) 657-664.
13. M. A. Balafar, A. R. Ramli, M. I. Saripan, S. Mashohor, Review of brain MRI image segmentation methods, Artif Intell Rev, DOI 10.1007/s10462-010-9155-0, 2010.
14. Balafar MA (2008) Medical image segmentation using fuzzy Cmean (FCM) and dominant grey levels of image. In: Visual information engineering conference , pp 314-317
15. Balafar M et al (2008a) Medical image segmentation using fuzzy C-mean (FCM), Bayesian method and user interaction. In: International conference on wavelet analysis and pattern recognition , pp 68-73
16. Balafar MA et al (2008b) New multi-scale medical image segmentation based on fuzzy c-mean (FCM). In: IEEE conference on innovative technologies in intelligent systems and industrial applications, pp 66-70
17. Balafar MA et al (2008c) Medical image segmentation using anisotropic filter, user interaction and fuzzy C-mean (FCM). In: Advanced intelligent computing theories and applications with aspects of contemporary intelligent computing techniques: 4th international conference on intelligent computing, Springer, pp 169-176
18. Balafar MA et al (2008d) Medical image segmentation using fuzzy C-mean (FCM), learning vector quantization (LVQ) and user interaction. In: Advanced intelligent computing theories and applications with aspects of contemporary intelligent computing techniques: 4th international conference on intelligent computing, Springer, p 177-184
19. Balafar MA et al (2008e) MRI segmentation of medical images using FCM with initialized class centers via genetic algorithm. In: International symposium on information technology , pp 1-4.
20. M.A. Balafar, ABD. Rahman ramli, M.Iqbal saripan, Syamsiah mashohor, Rozi Mahmud, MEDICAL IMAGE SEGMENTATION USING FUZZY C-MEAN (FCM) AND USER SPECIFIED DATA, Journal of Circuits, Systems, and Computers, 2010. DOI 10.1142/S0218126610005913.
21. M.A. Balafar, ABD. Rahman ramli, M.Iqbal saripan, Syamsiah mashohor, Rozi Mahmud, IMPROVED FAST FUZZY CMEAN AND ITS APPLICATION IN MEDICAL IMAGE SEGMENTATION, Journal of Circuits, Systems, and Computers, 2010. DOI 10.1142/S0218126610006001.
22. M.N. Ahmed, S.M. Yamany, N. Mohamed, A.A. Farag, T. Moriarty, A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data, IEEE Trans. Med. Imaging 21 (2002)193-199.
23. S.C. Chen, D.Q. Zhang, Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure, IEEE Trans. Systems Man Cybernet. B 34 (4) (2004) 1907-1916.
24. th Annual International Conference of IEEE EMBS, 2003, pp. 17-21.
25. Weiling Cai, Songcan Chen, Daoqiang Zhang, Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation, Pattern Recognition 40 (2007) 825-838
26. BrainWeb [Online]. Available: www.bic.mni.mcgill.ca/brainweb/
27. Zijdenbos, A., Dawant, B..: Brain segmentation and white matter lesion detection in MR images. Critical Reviews in Biomedical Engineering 1994; 22(5-6):401-465