Brain Tumor Segmentation on MRI Brain Images with Fuzzy Clustering and GVF Snake Model


  • Arthanari Rajendran Department of Electronics and Communication Engineering, Sriguru Institute of Technology, Coimbatore,Tamilnadu, India
  • Raghavan Dhanasekaran Syed Ammal Engineering College, Ramanathapuram, Tamilnadu, India


Deformable model, FCM, Segmentation, MRI image, GVF


Deformable or snake models are extensively used for medical image segmentation, particularly to locate tumor boundaries in brain tumor MRI images. Problems associated with initialization and poor convergence to boundary concavities, however, has limited their usefulness. As result of that they tend to be attracted towards wrong image features. In this paper, we propose a method that combine region based fuzzy clustering called Enhanced Possibilistic Fuzzy C-Means (EPFCM) and Gradient vector flow (GVF) snake model for segmenting tumor region on MRI images. Region based fuzzy clustering is used for initial segmentation of tumor then result of this is used to provide initial contour for GVF snake model, which then determines the final contour for exact tumor boundary for final segmentation. The evaluation result with tumor MRI images shows that our method is more accurate and robust for brain tumor segmentation.


M. Prastawa, E. Bullitt, S. Ho, G. Gerig, A brain tumor segmentation framework based on outlier detection, Medical Image Analysis, 2004, 18 (3), 217-231.

J.J. Corso, E. Sharon, A. Yuille, Multilevel segmentation and integrated Bayesian model classification with an application to brain tumor segmentation, in: MICCAI2006, Copenhagen, Denmark, Lecture Notes in Computer Science, October 2006,Vol. 4191, Springer, Berlin, pp. 790-798.

M.B. Cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J. Villemure, J.-P. Thiran, Atlas-based segmentation of pathological MR brain images using a model of lesion growth, IEEE Transactions on Medical Imaging, 2004,23 (10), 1301-1313.

J.-P. Thirion, Image matching as a diffusion process: an analogy with Maxwells demons, Medical Image Analysis, 1998,2 (3), 243-260.

G. Moonis, J. Liu, J.K. Udupa, D.B. Hackney, Estimation of tumor volume with fuzzyconnectedness segmentation of MR images, American Journal of Neuroradiology, 2002,23,352-363.

A.S. Capelle, O. Colot, C. Fernandez-Maloigne, Evidential segmentation scheme of multi-echo MR images for the detection of brain tumors using neighborhood information, Information Fusion, 2004, 5, 203-216.

W. Dou, S. Ruan, Y. Chen, D. Bloyet, J.M. Constans, A framework of fuzzy information fusion for segmentation of brain tumor tissues on MR images, Image and Vision Computing, 2007, 25,164-171.

M. Schmidt, I. Levner, R. Greiner, A. Murtha, A. Bistritz, Segmenting brain tumors using alignment-based features, in: IEEE Internat. Conf. on Machine learning and Applications, 2005, pp. 215-220.

J. Zhou, K.L. Chan, V.F.H Chong, S.M. Krishnan, Extraction of brain tumor fromMR images using one-class support vector machine, in: IEEE Conf. on Engineering in Medicine and Biology, 2005, pp. 6411-6414.

A. Lefohn, J. Cates, R. Whitaker, Interactive, GPU-based level sets for 3D brain tumor segmentation, Technical Report, University of Utah, April 2003.

Y. Zhu, H. Yang, Computerized tumor boundary detection using a Hopfield neural network, IEEE Transactions on Medical Imaging, 1997, 16 (1), 55-67.

S. Ho, E. Bullitt, G. Gerig, Level set evolution with region competition: automatic 3D segmentation of brain tumors, in: ICPR, Quebec, August 2002, pp. 532-535.

K. Xie, J. Yang, Z.G. Zhang, Y.M. Zhu, Semi-automated brain tumor and edema segmentation using MRI, European Journal of Radiology, 2005, 56, 12-19.

Wang Guoqiang, Wang Dongxue, Segmentation of Brain MRI Image with GVF Snake, Model,in: 2010 First International Conference on Pervasive Computing, Signal Processing and Applications,2010,pp.711-714.

Pal, N. R., Pal, K., Keller, J. M., and Bezdek, J. C.A, Possibilistic fuzzy c-means clustering algorithm, IEEE Transactions on Fuzzy Systems, 2005, 13(4), pp.517-530.

Buades A,Coll B,Morel J-M. "A non-local algorithm for image denoising",In CVPR 2005:60-5.

Ma, L. and Staunton, R. C., A modified fuzzy c-means image segmentation algorithm for use with uneven illumination patterns, Pattern Recognition, 2007, 40(11), pp.3005-3011.

C. Xu and J.L. Prince, Snakes, shapes, and gradient vector flow, IEEE Trans. on Image Processing, March 1998,vol. 7, pp. 359-369.

Bingrong Wu, Me Xie, Guo Li, Jingjing Gao, Medical Image Segmentation Based on GVF Snake Model IEEE Conference on Second International Intelligent Computation Technology and Automation (ICICTA 09), IEEE Press, 2009, vol. 1,Oct., pp. 637 - 640.

Zijdenbos, A. P., Dawant, B. M., Margolin, R. A., and Palmer, A. C. Morphometric analysis of white matter lesions in MR images: Method and validation, IEEE Transactions on Medical Imaging, 1994;13(4):716-724.



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