Fuzzy C means integrated with spatial information and contrast enhancement for segmentation of MR brain images

International Journal of Imaging Systems and Technology

Volumn 26, Issue 2, 2016, Pages 116-123

  Meena Prakash, R ^a   Shantha Selva Kumari, R ^b  

^a P S R ENGINEERING COLLEGE   (India)

^b MEPCO SCHLENK ENGINEERING COLLEGE   (India)

Author keywords

fuzzy C means; MR brain image segmentation; spatial information

Indexed keywords

BRAIN MAPPING; CEREBROSPINAL FLUID; CLUSTERING ALGORITHMS; FUZZY CLUSTERING; FUZZY SYSTEMS; MEMBERSHIP FUNCTIONS; PIXELS;

CEREBRO SPINAL FLUIDS; CONTRAST ENHANCEMENT; FUZZY C MEAN; FUZZY C-MEANS CLUSTERING ALGORITHMS; MR BRAIN IMAGE SEGMENTATION; SEGMENTATION ACCURACY; SPATIAL INFORMATIONS; STATE-OF-THE-ART METHODS;

IMAGE SEGMENTATION;

EID: 84975297743     PISSN: 08999457     EISSN: 10981098     Source Type: Journal    
DOI: 10.1002/ima.22166     Document Type: Article

References (29)

1. A. Demirhan and I. Guler, Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation, Eng Appl Artif Intell 24 (2011), 358–367.
2. B. Caldairou, N. Passat, P.A. Habas, C. Studholme, F. Rousseau, A non-local fuzzy segmentation method: Application to brain MRI, Pattern Recogn 44 (2011), 1916–1927.
3. C.M. Bishop, Pattern recognition and machine learning, Springer, New York, 2006.
4. F. Zhao, H. Liu, and J. Fan, A multiobjective spatial fuzzy clustering algorithm for image segmentation, Appl Soft Comput 30 (2015), 48–57.
5. H. Verma, R.K. Agrawal, and N. Kumar, Improved fuzzy entropy clustering algorithm for MRI brain image segmentation, Int J Imaging Syst Technol 24 (2014), 277–283.
6. J. Ashburner and K.J. Friston, Unified segmentation, NeuroImage 26 (2005), 839–851.
7. J. Bezdek, Pattern recognition with fuzzy objective function algorithms, Plenum, New York, 1981.
8. J. Dunn, A fuzzy relative of the ISODATA process and its use in detecting compact, well-separated clusters, J Cybernet 3 (1974), 32–57.
9. J. Zhang and W. Jiang, Segmentation for brain magnetic resonance images using dual-tree complex wavelet transform and spatial constrained self-organizing tree map, Int J Imaging Syst Technol 24 (2014), 208–214.
10. K. Kazemi and N. Noorizadeh, Quantitative comparison of SPM, FSL, and brainsuite for brain MR image segmentation, J Biomed Phys Eng 4 (2014), 13–26.
11. L. Chen, C.L. Philip Chen, and M. Lu, A multiple-Kernel Fuzzy C-means algorithm for image segmentation, IEEE Trans Syst Man Cybernetics-Part B: Cybernetics 41 (2011), 1263–1274.
12. M.A. Balafar, A.B.D. Rahman Ramli, M.I. Saripan, and S. Mashohor, Medical image Segmentation using Fuzzy C-Mean (FCM) and user specified data, J Circuit Syst Comput 19 (2010), 1–14.
13. M.A. Balafar, Fuzzy C-mean based brain MRI segmentation algorithms, Artif Intell Rev 41 (2014), 441–449.
14. M.N. Ahmed, S.M. Yamany, N. Mohamed, A.A. Farag, and 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.
15. M.Y. Siyal and L. Yu, An intelligent modified fuzzy, c-means based algorithm for bias estimation and segmentation of brain MRI, Pattern Recogn Lett 26 (2005), 2052–2062.
16. Q. Mahmood, A. Chodorowski, and M. Persson, Automated MRI brain tissue segmentation based on mean shift and fuzzy c-means using a priori tissue probability maps, IRBM 36 (2015), 185–196.
17. S. Agrawal, R. Panda, and L. Dora, A study on fuzzy clustering for magnetic resonance brain image segmentation using soft computing approaches, Appl Soft Comput 24 (2014), 522–533.
18. S. Chen and D. Zhang, Robust image segmentation using FCM with spatial constraints based on new Kernel-induced distance measure, IEEE Trans Syst, Man, and Cybernetics-Part B: Cybernetics 34 (2004), 1907–1916.
19. S. Madhukumar and N. Santhiyakumari, Evaluation of k-Means and fuzzy C-Means segmentation of MR images of brain, Egypt J Radiol Nucl Med 46 (2015), 475–479.
20. S. Ramathilagam, R. Pandiyarajan, A. Sathya, R. Devi, and S.R. Kannan, Modified fuzzy, c-means algorithm for segmentation of T1-T2-weighted brain MRI, J Comput Appl Math 235 (2011), 1578–1586.
21. S.K. Adhikari, J.K. Sing, D.K. Basu, and M. Nasipuri, Conditional spatial fuzzy, c-means clustering algorithm for segmentation of MRI images, Appl Soft Comput 34 (2015), 758–769.
22. V. Sergi, O. Arnau, C. Mariano, R. Eloy, and, L. Xavier, Comparison of 10 brain tissue segmentation methods using revisited IBSR annotations, J Magn Reson Imaging 41 (2014), 93–101.
23. X. Lu, J. Wu, X. Ren, B. Zhang, and Y. Li, The Study and application of the improved region growing algorithm for liver segmentation, Optik 125 (2014), 2142–2147.
24. Y. Chen, J. Zhang, S. Wang, and Y. Zheng, Brain magnetic resonance image segmentation based on an adapted non-local fuzzy, c-means method, IET Computer Vision 6 (2012), 610–625.
25. Y. Li and Y. Shen, Fuzzy c-means clustering based on spatial neighborhood information for image segmentation, J Syst Eng Electron 21 (2010), 323–328.
26. Y. Zhang, M. Brady, and S. Smith, Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm, IEEE Trans Med Imaging 20 (2001), 45–57.
27. Z. Feng, H. Liu, and J. Fan, A multiobjective fuzy clustering algorithm based on robust local spatial information for image segmentation, Lecture Note Comput Sci 8261 (2013), 505–512.
28. Z. Zaixin, C. Lizhi, and C. Guangquan, Neighborhood weighted fuzzy c-means clustering algorithm for image segmentation, IET Image Process 8 (2014), 150–161.
29. Z.-X. Ji, Q.-S. Sun, and D.-S. Xia, A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image, Comput Med Imaging Graphics 35 (2011), 383–397.