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A Novel Multiple-kernel based Fuzzy c-means Algorithm with Spatial Information for Medical Image Segmentation
Venu Nookala, B.Anuradha
Pages - 286 - 301 | Revised - 15-05-2013 | Published - 30-06-2013
Published in International Journal of Image Processing (IJIP)
MORE INFORMATION
KEYWORDS
FCM, Image Segmentation, Gaussian Kernal, Fuzzy, Multiple-Kernal.
ABSTRACT
Fuzzy c-means (FCM) algorithm has proved its effectiveness for image segmentation. However,
still it lacks in getting robustness to noise and outliers, especially in the absence of prior
knowledge of the noise. To overcome this problem, a generalized a novel multiple-kernel fuzzy cmeans
(FCM) (NMKFCM) methodology with spatial information is introduced as a framework for
image-segmentation problem. The algorithm utilizes the spatial neighborhood membership values
in the standard kernels are used in the kernel FCM (KFCM) algorithm and modifies the
membership weighting of each cluster. The proposed NMKFCM algorithm provides a new
flexibility to utilize different pixel information in image-segmentation problem. The proposed
algorithm is applied to brain MRI which degraded by Gaussian noise and Salt-Pepper noise. The
proposed algorithm performs more robust to noise than other existing image segmentation
algorithms from FCM family.
1 | Qureshi, A. N. A. (2015). Computer aided assessment of CT scans of traumatic brain injury patients. |
2 | Mahajan, S. M., & Dubey, Y. K. (2015, April). Color Image Segmentation Using Kernalized Fuzzy C-means Clustering. In Communication Systems and Network Technologies (CSNT), 2015 Fifth International Conference on (pp. 1142-1146). IEEE. |
3 | Venu, N., & Anuradha, B. Nav view search. |
4 | Venu, N., & Anuradha, B. (2014). Multi-Hyperbolic Tangent Fuzzy C-means Algorithm for MRI Segmentation. |
5 | Liu Jianwei, Guo & Ray (2014). Brain histogram image segmentation strategy. Xi'an University of Technology, 34 (3), 188-192. |
6 | Venu, N., & Anuradha, B. (2013). PSNR Based Fuzzy Clustering Algorithms for MRI Medical Image Segmentation. International Journal of Image Processing and Visual Communication, 2(2), 01-07. |
. Arthur D,Vassilvitskii S,"How Slow is the k-means Method?," Proceedings of the 2006 Symposium on Computational Geometry , June. 2006. | |
. D. Pham, “An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities,” Pattern Recognition Letters, vol. 20, pp. 57–68, 1999. | |
. D. Pham, “Fuzzy clustering with spatial constraints,” in Proceedings of International Conference on Image Processing, New York, 2002, vol. II, pp. 65–68. | |
. D. Pham, C. Xu, and J. Prince, “A survey of current methods in medical image segmentation,” In Annual Review of Biomedical Engineering, vol. 2, pp. 315–337, 2000. | |
. D. S.Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C.Morris, and R. L. Buckner,Open access series of imaging studies (OASIS): Crosssectional MRI data in young,middle aged, nondemented, and demented older adults. J. Cogn. Neurosci., 19 (9) 1498–1507, 2007. | |
. G. Camps-Valls, L. Gomez-Chova, J. Munoz-Mari, J. L. Rojo-Alvarez, and M. MartinezRamon,“Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection,” IEEE Trans. Geosci. Remote Sens., vol. 46, no. 6,pp. 1822–1835, Jun. 2008. | |
. G. Karmakar and L. Dooley, “A generic fuzzy rule based image segmentation algorithm,”Pattern Recognition Letters., vol. 23, no. 10, pp.1215–1227, 2002. | |
. J. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms,” Kluwer Academic Publishers, New York: Plenum, 1981. | |
. J. Noordam, W. van den Broek, and L. Buydens, “Geometrically guided fuzzy C-means clustering for multivariate image segmentation,” in Proceedings of the International Conference on Pattern Recognition, 2000, vol. 1, pp.462–465. | |
. J. Udupa and S. Samarasekera, “Fuzzy connectedness and object definition: Theory,algorithm and applications in image segmentation,” Graphical Models and Image Processing, vol. 58, no. 3, pp. 246–261, 1996. | |
. L. Szilagyi, Z. Benyo, S. Szilagyii, and H. Adam, “MR brain image segmentation using an enhanced fuzzy C-means algorithm,” in Proceedings of the 25" Annual International Conference of the IEEE EMBS, pp. 17–21, 2003. | |
. L. Zadeh, “Fuzzy sets,” Inf. Control, vol. 8, pp. 338–353, 1965. | |
. László Szilágyi, Sándor M. Szilágyi, Balázs Benyó and Zoltán Benyó, “Application of Hybrid c-Means Clustering Models in Inhomogeneity Compensation and MR Brain Image Segmentation,” 5th International Symposium on Applied Computational Intelligence and Informatics ,pp.105-110, May. 2009. | |
. Long Chen, C. L. Philip Chen, and Mingzhu Lu, “A Multiple-Kernel Fuzzy C-Means Algorithm for Image Segmentation,” IEEE Trans. Systems, Man, And Cybernetics—Part B: Cybernetics, vol. 41, No. 5, pp. 1263 – 1274, February 9,2011. | |
. M. Ahmed, S. Yamany, N. Mohamed, A. Farag, and T. Moriarty, “A modified fuzzy Cmeans algorithm for bias field estimation and segmentation of MRI data,” IEEE Transactions on Medical Imaging, vol. 21, no. 3, pp. 193–199, 2002. | |
. M. Krinidis and I. Pitas, “Color texture segmentation based-on the modal energy of deformable surfaces,” IEEE Transactions on Image Processing, vol. 18, no. 7, pp. 1613–1622, Jul. 2009. | |
. M. Yang, Y. J. Hu, K. Lin, and C. C. Lin, “Segmentation techniques for tissue differentiation in MRI of ophthalmology using fuzzy clustering algorithms,” Magnetic Resonance Imaging, vol. 20, no. 2, pp. 173–179, 2002. | |
. MacQueen,J.B. “Some Methods for classification and Analysis of Multivariate Observations,"Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, pp. 281–297, 1967. | |
. Masulli, F., Schenone, A., 1999. A fuzzy clustering based segmentation system as support to diagnosis in medical imaging. Artif. Intell. Med. 16, 129–147. | |
. Miin-Shen Yang, Hsu-Shen Tsai, “A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction,” Pattern Recognition Letters, vol. 29, pp. 1713–1725, May 2008. | |
. Mohammad Ali Balafar, Abd.Rahman Ramli, M.Iqbal Saripan, Syamsiah Mashohor,“Medical Image Segmentation Using Fuzzy C-Mean (Fcm), Bayesian Method And User Interaction,” Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, pp. 68-73, Aug. 2008. | |
. S. Chen and D. Zhang, “Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure,” IEEE Transactions on Systems, Man,and Cybernetics, vol. 34, pp. 1907–1916, 2004. | |
. W. Cai, S. Chen, and D. Zhang, “Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation,” Pattern Recognition, vol. 40, no.3, pp. 825–838, Mar. 2007. | |
. X. Munoz, J. Freixenet, X. Cufi, and J. Marti, “Strategies for image segmentation combining region and boundary information,” Pattern Recognition Letters, vol. 24, no. 1,pp. 375–392, 2003. | |
. Y. Tolias and S. Panas, “Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions,” IEEE Transactions on Systems,Man, and Cybernetics, vol. 28, no. 3, pp. 359–369, Mar.1998. | |
. Zhang, D.Q., Chen, S.C., 2004. A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artif. Intell. Med. 32, 37–50. | |
Mr. Venu Nookala
Alfa college of Engg. - India
venun70@gmail.com
Dr. B.Anuradha
Sir Venkateswara University - India
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