The week's pick
Random Articles
Reseach Article
Automatic MR Brain Tumor Detection using Possibilistic C-Means and K-Means Clustering with Color Segmentation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 56 - Number 2 |
| Year of Publication: 2012 |
| Authors: Hema Rajini.n, Bhavani.r |
10.5120/8862-2825
|
Hema Rajini.n, Bhavani.r . Automatic MR Brain Tumor Detection using Possibilistic C-Means and K-Means Clustering with Color Segmentation. International Journal of Computer Applications. 56, 2 ( October 2012), 11-17. DOI=10.5120/8862-2825
Abstract
Magnetic resonance imaging is often the medical imaging method of choice when soft-tissue delineation is necessary. This paper presents a new approach for automated detection of brain tumor based on k-means and possibilistic c-means clustering with color segmentation, which separates brain tumor from healthy tissues in magnetic resonance images. The magnetic resonance feature images used for the tumor detection consist of T1-weighted and T2-weighted images for each axial slice through the head. The proposed method consists of three stages namely pre-processing, segmentation and feature extraction. In the first stage, we have suppressed the noise using image filtering. In the second stage, segmentation is computed using an unsupervised k-means and possibilistic c-means clustering algorithm with color conversion. The segmentation accuracy is obtained using the silhouette method. The experimental results show the superiority of the possibilistic c-means clustering method. In the third stage, the key features are extracted using the threshold. The application of the proposed method for tracking tumor is demon¬strated to help pathologists distinguish exactly tumor size and region.
References
- Ricci, P. E. and Dungan, D. H. 2001. Imaging of low and intermediate-grade gliomas. SEMRADONC, 11(2), 103-112.
- Armstrong, T. S. , Cohen, M. Z. , Weinbrg, J. and Gilbert, M. R. 2004. Imaging techniques in neuro oncology. SEMONCNUR, 20(4): 231-239.
- Prasad, P. V. 2006. MRI: Methods and Biologic Applications, Humana Press Inc.
- Archibald, R. , Chen, K. , Gelb, A. and Renaut, R. 2003. Improving tissue segmentation of human brain MRI through pre-processing by the Gegenbauer reconstruction method. Neuroimage 20: 489–502.
- Gibbs, P, Buckley, D. L, and Blackband, S. J. 1996. Tumour volume determination from MR images by morphological segmentation. Phys Med Biol, 41(11): 2437-2446.
- Letteboer, M. M. J, Olsen, O. F and Dam, E. B. 2004. Segmentation of tumors in magnetic resonance brain images using an interactive multiscale watershed algorithm. Acad Radiol, 11: 1125-1138.
- Droske, M, Meyer, B and Rumpf, M. 2005. An adaptive level set method for interactive segmentation of in tracranial tumors. Neuro Res, 27(4): 363-370.
- Fletcher-Heath, L. M, Hall, L. O and Goldgof, D. B. 2001. Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artif Intell Med, 21(1-3): 43-63.
- Zou, K. H, Wells, W. M and Kikinis, R. 2004. Three validation metrics for automated probabilistic image segmentation of brain tumours. Stat Med, 23(8): 1259-1282.
- Clark, M. C, Lawrence, L. O, Golgof, D. B, Velthuizen, R, Murtagh, F. R and Silbiger, M. S. 1998. Automatic tumor segmentation using knowledge-based techniques," IEEE Transactions on Medical Imaging, 17(2): 187–201.
- Dou, W, Ruan, S, Chen, Y, Bloyet, D and Constans, J. M. 2007. A framework of fuzzy information fusion for segmentation of brain tumor tissues on MR images. Image and Vision Computing, 25: 164–171.
- Lefohn, A, Cates, J and Hitaker, R. 2003. Interactive, GPU-based level sets for 3D brain tumor segmentation. Technical Report, University of Utah.
- Zook, J and Iftekharuddin, K. M. 2005. Statistical analysis of fractal-based techniques in brain tumor detection. Magn. Reson. Imaging, 23(5): 671-678.
- Anirban, M and Ujjwal, M. 2011. A multiobjective approach to MR brain image segmentation. Applied Soft Computing, 11: 872–880.
- Cheng, H. D, Shan, J, Ju, W, Guo, Y and Zhang, L. 2010. Automated breast cancer detection and classification using ultrasound images: A survey. Pattern Recognition, 43: 299–317.
- Bankman, I. N. 2000. Hand Book of Medical Imaging. John Hopkins University.
- Kannan, S. R. 2008. A new segmentation system for brain MR images based on fuzzy techniques. Applied Soft Computing, 8(4): 1599–1606.
- Fazel Zarandi, M. H, Zarinbal, M and Turksen, I. B. 2009. Type-II possibilistic C-mean clustering. IFSA-USEFLAT, 30–35.
- Vellingiri, J and Chenthur Pandian, S. 2011. Fuzzy possibilistic c-means algorithm for clustering on web usage mining to predict the user behaviour. European Journal of Scientific Research, 58(2): 222-230.
- Krishnapuram, R and Keller, J. M. 1993. A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems, 1: 98–110.
- Gustafson, D. E and Kessel, W. C. 1978. Fuzzy clustering with a fuzzy covariance matrix. Proceedings of the 17th Symposium on Adaptive Processes, 17: 761–766.
- Liang, Q and Mendel, J. M. 2000. Interval Type-2 fuzzy logic systems: theory and design. IEEE Transactions on Fuzzy Systems, 8: 535–550.
- Kannan S. R, Ramathilagam. S, Sathya. A, Pandiyarajan. R. 2010. Effective fuzzy c-means based kernel function in segmenting medical images. Computers in Biology and Medicine, 40: 572–579.
Index Terms
Keywords