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A Comparative Analysis of MRI Brain Tumor Segmentation Technique

by Anubha Lakra, R.B. Dubey
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 125 - Number 6
Year of Publication: 2015
Authors: Anubha Lakra, R.B. Dubey
10.5120/ijca2015905922

Anubha Lakra, R.B. Dubey . A Comparative Analysis of MRI Brain Tumor Segmentation Technique. International Journal of Computer Applications. 125, 6 ( September 2015), 5-14. DOI=10.5120/ijca2015905922

@article{ 10.5120/ijca2015905922,
author = { Anubha Lakra, R.B. Dubey },
title = { A Comparative Analysis of MRI Brain Tumor Segmentation Technique },
journal = { International Journal of Computer Applications },
issue_date = { September 2015 },
volume = { 125 },
number = { 6 },
month = { September },
year = { 2015 },
issn = { 0975-8887 },
pages = { 5-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume125/number6/22434-2015905922/ },
doi = { 10.5120/ijca2015905922 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:15:17.754317+05:30
%A Anubha Lakra
%A R.B. Dubey
%T A Comparative Analysis of MRI Brain Tumor Segmentation Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 125
%N 6
%P 5-14
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Magnetic Resonance Imaging (MRI) is a powerful visualization tool that permits to acquire images of internal anatomy of human body in a secure and non-invasive manner. The important task in the diagnosis of brain tumor is to determine the exact location, orientation and area of the abnormal tissues. This paper presents a performance analysis of image segmentation techniques, viz., Genetic algorithm, K-Means Clustering and Fuzzy C-Means clustering for detection of brain tumor from brain MRI images. The performance evaluation of these techniques is carried out on the real time database on the basis of error percentage compared to ground truth.

References
  1. H. Hooda, O. Prakash, T. Singhal “Brain tumor segmentation:a performance analysis using k-means, fuzzy cmeans and region growing algorithm”, IEEE International Conference on Advanced Communication and Computing Technologies, vol. 2, pp.1621-1626, 2014.
  2. S. Nag, I. Kanta, M. S. Roy and S. K. Bandyopadhyay, "A review of image segmentation methods on brain MRI for detection of tumor and related abnormalities", International Journal of Advanced Research in Computer Science and Software engineering, vol.4, Issue5, pp. 1073-1095, 2014.
  3. I. Soesanti, A. Susanto, T .S. Widodo and M. Tjokronagoro, "Optimized fuzzy logic application for MRI brain image segmentation", International Journal of Computer science and Information technology, vol. 3, no. 5, pp. 137-146, 2011.
  4. F. Hoseyni, S. Haghipour and A. Sorkhabi, " Improvement of segmentation on MRI images using fuzzy clustering C-means and watershed marker control algorithm", Indian Journal Science Research, vol. 4,no. 3, pp. 477-451, 2014.
  5. R. P. Joseph, C. S. Singh and M. Manikandan, "Brain tumor MRI image segmentation and detection in image segmentation", International Journal of Research in Engineering and Technology, vol. 3, Issue 1, pp. 1-5, 2014.
  6. M. Yambal, H. Gupta, "Image segmentation using fuzzy C-means clustering :A survey", International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, Issue 7, pp. 2319-5490, 2013.
  7. I. Soesanti, A. Susanto, T .S. Widodo and M. Tjokronagoro, "Optimized fuzzy logic application for MRI brain image segmentation", International Journal
  8. of Computer science and Information technology, vol. 3, no. 5, pp. 137-146, 2011.D. Q. Zhang, S. C. Chen, "A novel kernelized fuzzy c-means algorithm with
  9. application in medical image segmentation", Elsevier, vol. 32, Issue 1, pp. 37-50, 2004.
  10. M. Yang, Y. Hu K. C. Lin and C. C. Lin, "Segmentation techniques for tissue differentiation in MRI of opthalmology using fuzzy clustering algorithms", Elservier, vol. 20, no. 2, pp. 173-179, 2002.
  11. A. Rajendran, R. Dhanasekaran, "Brain tumor segmentation on MRI images with fuzzy clustering and GVF snake model", International Journal Computer Communication, vol. 7, no. 3, pp. 530-539, 2012.
  12. A. W. Liew, H. Yan, "An adaptive spatial fuzzy clustering algorithm for 3-d MRI image segmentation", IEEE transactions on Medical Imaging, vol. 22, no. 9, pp. 1063-1074, 2003.
  13. D. L. Pham, C. Xu, J. L .prince, "A survey of current methods in medical image segmentation", Annual Review of Biomedical Engineering, vol. 2, pp. 1-27, 1998.
  14. M. Sharma, S. Mukherjee, "Fuzzy c-means, ANFIS and genetic algorithm for segmenting astrocytoma- A type of brain tumor", International Journal of Advanced Research in Computer Science and software engineering, vol. 3, Issue 6, pp. 852-858, 2013.
  15. E. A. Zanaty, A.S. Ghiduk, "A novel approach based on genetic algorithms and region growing for magnetic resonance image segmentation", vol. 10, no. 3, pp. 1302-1342, 2013.
  16. S. Khare, N. Gupta and V. Srivastava, "Genetic algorithm employed to detect brain tumor in MRI image", International Conference on Cloud, Big Data and Trust, vol. 3, pp. 59-64, 2013.
  17. S. Saha, S. Bandyopadhyay, "Mri image segmentation by fuzzy symmetry based genetic clustering tech", IEEE Congress on Evolutionary computation, vol.10, no.6, pp. 4417-4424, 2007.
  18. N. Senthilk and R. Rajesh, " Edge detection techniques for image segmentation-A survey of soft computing approaches", International Journal of Recent Trends Engineering and Technology, vol. 1, no. 2, pp. 250-254, 2009.
  19. T. F. Chan, S. Esedoglu, and M. Nikolova, "Algorithms for finding global minimizers of image segmentation and denoising models", Society for Industrial and applied Mathematics, vol. 66, no. 5, pp. 1632-1648, 2004.
  20. D. C. Dhanwaani, M. M. Bartere, “Survey on various techniques of brain tumor detection from MRI images", International Journal of Computational Engineering Research, vol.4, Issue1, pp. 24-26, 2014.
  21. S. Chabrier, C. Rosenberger, B. Emile and H. Laurent, "Optimization based image segmentation by genetic algorithm", Journal on Image and Video Processing, vol. 10, no. 8, pp. 1-10, 2008.
  22. G. Szekely, A. Lelemen, C. Brechbuler and G. Gerig, "Segmentation of 2-d and 3-d objects from mri volume data using constrained elastic deformations of flexible fourier contour and surface models", Medical Image Analysis, vol.1, no.1, pp.19-34, 1996.
  23. S. K. Bandhyopadhyay, T. U. Paul, "Segmentation of brain MRI image-A review ",International Journal of Advanced Research in Computer Science and Software engineering, vol.2, Issue3, pp. 409-413, 2012.
  24. D. Selvaraj, R. Dhanan, "Mri brain image segmentation techniques-A review", Indian national of Computer Science and Engineering, vol.4, no.5, pp. 364-381, 2013.
  25. R. Agrawal, M. Sharma, "Review of segmentation methods for brain tissue with magnetic resonance images", International Journal of Computer Network and Information Security, vol. 4, no. 5, pp. 55-62, 2014.
  26. A. Ahir, " Study of techniques used for medical image segmentation and computation of test for region classification of brain MRI", International Journal of Information Technology and Computer Science, vol. 5, no. 6, pp. 44-52, 2013.
  27. S. Nag, I. Kanta, M. S. Roy and S. K. Bandyopadhyay, "A review of image segmentation methods on brain MRI for detection of tumor and related abnormalities", International Journal of Advanced Research in Computer Science and Software engineering, vol.4, Issue5, pp. 1073-1095, 2014.
  28. D. Kaushik, U. Singh and P. Singhal, " Brain tumor segmentation using genetic algorithm", International Journal of Computer Applications, vol. 4, no. 3, pp. 309-311,2014.
  29. A. Kaur and G. Jindal, " Tumor detection using genetic algorithm", International Journal of Computer Science and Technology, vol. 4, Issue 1, pp. 423-427,2013.
  30. S. Janardhana, “Detection of suspicious region in medical images by genetic algorithm”, International Conference on Current Trends in Engineering and Technology, vol.5, no.3, pp. 25-28, 2013.
  31. R. S. Kabade, M. S. Gaikwad, " Segmentation of brain tumor and its area calculation in brain MR images using k-means clustering and fuzzy c-means algorithm", International Jorunal of Computer Science Engineering Technology, vol. 4, no. 5, pp. 524-531, 2013.
  32. P. Dhanalakshmi and T. Kanimozhi, "Automatic segmentation of brain tumor using k- means clustering and its area calculation", Interantional Journal of Advanced Electrical and Electronics Engineering, vol. 2, Issue 2, pp. 130-134, 2013.
  33. K. S. Angel Viji, Dr J. Jayakumari “Modified texture based region growing segmentation of mr brain images”, IEEE Conference on Information and Communication Technologies, vol. 1, pp. 691-695, 2013.
  34. S. Z. Oo1, A. S. Khaing, "Brain tumor detection and segmentation using watershed segmentation and morphological operation'', International Journal of Research in Engineering and Technology, vol.3, no. 2, pp. 368-374, 2014.
  35. M. Karuna1, A. Joshi, "Automatic detection and serverity analysis of brain tumors using GUI in matlab” , International Journal of Research in Engineering and Technology, vol. 2, no. 2, pp. 587-591, 2013.
  36. R. C. Patil, Dr. A. S. Bhalchandra, " Brain tumor extraction from MRI images using matlab” International Journal of Electronics, Communication & Soft Computing Science and Engineering , vol. 2, no. 3, pp.1-4, 2013.
  37. S. Hanardhana, J. Jaya, K. J. Sabareesaan, J. George and D. Yokeshwaran, "Detection of suspicious region in medical images by genetic algorithm", IEEE International Conference on Current Trends in Engineering and Technology, vol. 2, no.3, pp. 28, 2013.
  38. A. Kaur and G. Jindal, " Tumor detection using genetic algorithm", International Journal of Computer Science and Technology, vol. 4, Issue 1, pp. 423-427,2013.
  39. D. Kaushik, U. Singh and P. Singhal, " Brain tumor segmentation using genetic algorithm", International Journal of Computer Applications, vol. 4, no. 3, pp. 309-311,2014.
  40. A. Kaur and G. Jindal, "Overview of tumor detection using genetic algorithm", International Journal of Innovations in Engineering and Technology, vol. 2, Issue 2, pp. 348-352, 2013.
  41. P. Dhanalakshmi and T. Kanimozhi, "Automatic segmentation of brain tumor using k- means clustering and its area calculation", Interantional Journal of Advanced Electrical and Electronics Engineering, vol. 2, Issue 2, pp. 130-134, 2013.
  42. R. S. Kabade, M. S. Gaikwad, " Segmentation of brain tumor and its area calculation in brain MR images using k-means clustering and fuzzy c-means algorithm", International Jorunal of Computer Science Engineering Technology, vol. 4, no. 5, pp. 524-531, 2013.
  43. B. Brundha and M. K. Nagendra, " MRI segmentation of brain to detect brain tumor and its area calculation using K-means clustering and fuzzy c-means algorithm", International Journal For Technological Research In Engineering, vol. 2, Issue 9, pp. 1781-1785,2015.
Index Terms

Computer Science
Information Sciences

Keywords

MRI brain tumor segmentation Genetic algorithm K-means clustering and Fuzzy C-means clustering.

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