Brain Tumor Segmentation Using K-Means Clustering and Fuzzy C-Means Algorithms

Authors

  • K. Gayathri  Department of Electronics and Communication Engineering, IFET college of Engineering, Villupuram, Tamil Nadu, India
  • D. Vasanthi  Department of Electronics and Communication Engineering, IFET college of Engineering, Villupuram, Tamil Nadu, India

Keywords:

Magnetic Resonance Image (MRI), Fuzzy C Means Algorithm (FCM), K-Means Algorithm.

Abstract

Tumor is an uncontrolled growth of tissue in any part of the body. The tumor is of different types and they have different characteristics and different treatment. Normally the anatomy of the brain can be viewed by the MRI scan or CT scan. MRI scanned image is used for the entire process. The MRI scan is more comfortable than any other scans for diagnosis. It will not affect the human body, because it doesn’t practice any radiation. It is centered on the magnetic field and radio waves. After the segmentation, which is done through k-means clustering and fuzzy c-means algorithms the brain tumor is detected and its exact location is identified.FCM with k-means clustering algorithms is used to increase the accuracy ratio of tumor detection system. The tumor area is calculated for accurate result.

References

  1. Ruchita A. Banchpalliwar 1, Dr. Suresh S. Salankar, "Diagnosis of Brain Tumor Through MRI Image Processing using Clustering with Optimization Technique" , ", IEEE Vol (4), Issue 4, April 2016
  2. Ganesh S. Raghtate, Suresh S. Salankar, "Modified Fuzzy C Means With Optimized Ant Colony Algorithm For Image Segmentation" 2015 International Conference on Computational Intelligence and Communication Networks.
  3. Mohammed Sabbih Hamoud Al-Tamimi Ghazali Sulong, "Tumor Brain Detection Through Mr Images: A Review Of Literature", Journal Of Theoretical And Applied Information Technology 20th April 2014. Vol. 62 No.2.
  4. Sayali D. Gahukar et al Int. Journal of Engineering Research and Applications Vol. 4, Issue 4( Version 5), April 2014, pp.107-111
  5. K.Selvanayaki, Dr.P.Kalugasalam Intelligent Brain Tumor Tissue Segmentation From Magnetic Resonance Image (Mri) Using Meta Heuristic Algorithms Journal Of Global Research In Computer Science Volume 4, No. 2, February 2013 Research , Volume 2,Issue 6,June 2013,Pp 626-632.
  6. S.Roy And S.K.Bandoyopadhyay, "Detection And Qualification Of Brain Tumor From Mri Of Brain And Symmetric Analysis", International Journal Of Information And Communication Technology Research, Volume 2 No.6, June 2012, Pp584-588
  7. A.R.Kavitha, Dr.C.Chellamuthu, Ms.Kavin Rupa, "An Efficient Approach for Brain Tumour Detection Based on Modified Region Growing and Network in MRI Images",Information Forensics and Security, IEEE Transactions on, Vol.9 (2), May 2012.
  8. Wen-Liange, De-Hua Chen, Mii-Shen Yang, "Suppressed fuzzy-soft learning vector quantization for MR Segmentation", Elsevier Ltd, Vol 52, Issue 1,Pag: 33-43, May2011.
  9. R.B.Dubey, M.Hanmandlu, Sr.Member, Shantaram Vasikarla, "Evaluationof Three Methods for MRI Brain Tumor segmentation", IEEE Digital Object Identifier: 10.1109/ITNG.2011.92,2011.
  10. Shaheen Ahmed, Khan M.Iftekharuddin, "Efficacy of Texture, Shape and Intensity Feature Fusion for Posterior Fossa Tumor Segmentation InMRI", IEEE Vol (2), pag: 206-13, Mar 2011.
  11. David Rivest-Henault, Mohamed Cheriet," Unsupervised MRI segmentation of brain tissues Using a local linear model and set", Elsevier,Vol 29, Issue 2, pag.243-259, Mar2011.
  12. Vida Harati, Rasoul Khayati, Abdolreza Farzan, "Fully automated tumor segmentation based on animproved fuzzy connectedness Algorithm in BrainMR Images", Elsevier Ltd,Vol 7, pag: 483-92, May 2011.

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Published

2017-04-30

Issue

Section

Research Articles

How to Cite

[1]
K. Gayathri, D. Vasanthi, " Brain Tumor Segmentation Using K-Means Clustering and Fuzzy C-Means Algorithms, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 2, pp.704-707, March-April-2017.