CFP last date
22 July 2024
Call for Paper
August Edition
IJCA solicits high quality original research papers for the upcoming August edition of the journal. The last date of research paper submission is 22 July 2024

Submit your paper
Know more
The week's pick
Responsive image
A semi-parallel data acquisition method in electrical impedance tomography using undersampling technique

Adriano Regis Daniel J. Pagano Francisco R.M. Mota Marduck M. Henao
Random Articles
Reseach Article

Automatic MR Brain Tumor Detection using Possibilistic C-Means and K-Means Clustering with Color Segmentation

by Hema Rajini.n, Bhavani.r
journal cover thumbnail

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

@article{ 10.5120/8862-2825,
author = { Hema Rajini.n, Bhavani.r },
title = { Automatic MR Brain Tumor Detection using Possibilistic C-Means and K-Means Clustering with Color Segmentation },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 56 },
number = { 2 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 11-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume56/number2/8862-2825/ },
doi = { 10.5120/8862-2825 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:57:50.081908+05:30
%A Hema Rajini.n
%A Bhavani.r
%T Automatic MR Brain Tumor Detection using Possibilistic C-Means and K-Means Clustering with Color Segmentation
%J International Journal of Computer Applications
%@ 0975-8887
%V 56
%N 2
%P 11-17
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
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
  1. Ricci, P. E. and Dungan, D. H. 2001. Imaging of low and intermediate-grade gliomas. SEMRADONC, 11(2), 103-112.
  2. Armstrong, T. S. , Cohen, M. Z. , Weinbrg, J. and Gilbert, M. R. 2004. Imaging techniques in neuro oncology. SEMONCNUR, 20(4): 231-239.
  3. Prasad, P. V. 2006. MRI: Methods and Biologic Applications, Humana Press Inc.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. Lefohn, A, Cates, J and Hitaker, R. 2003. Interactive, GPU-based level sets for 3D brain tumor segmentation. Technical Report, University of Utah.
  13. Zook, J and Iftekharuddin, K. M. 2005. Statistical analysis of fractal-based techniques in brain tumor detection. Magn. Reson. Imaging, 23(5): 671-678.
  14. Anirban, M and Ujjwal, M. 2011. A multiobjective approach to MR brain image segmentation. Applied Soft Computing, 11: 872–880.
  15. 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.
  16. Bankman, I. N. 2000. Hand Book of Medical Imaging. John Hopkins University.
  17. Kannan, S. R. 2008. A new segmentation system for brain MR images based on fuzzy techniques. Applied Soft Computing, 8(4): 1599–1606.
  18. Fazel Zarandi, M. H, Zarinbal, M and Turksen, I. B. 2009. Type-II possibilistic C-mean clustering. IFSA-USEFLAT, 30–35.
  19. 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.
  20. Krishnapuram, R and Keller, J. M. 1993. A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems, 1: 98–110.
  21. 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.
  22. Liang, Q and Mendel, J. M. 2000. Interval Type-2 fuzzy logic systems: theory and design. IEEE Transactions on Fuzzy Systems, 8: 535–550.
  23. 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

Computer Science
Information Sciences

Keywords

Magnetic Resonance Imaging (MRI) K-means clustering Segmentation Possibilistic C-means clustering Tumor

Powered by PhDFocusTM