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Reseach Article

MRI Brain Image Segmentation based on Wavelet and FCM Algorithm

by Iraky Khalifa, Aliaa Youssif, Howida Youssry
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 47 - Number 16
Year of Publication: 2012
Authors: Iraky Khalifa, Aliaa Youssif, Howida Youssry
10.5120/7275-0446

Iraky Khalifa, Aliaa Youssif, Howida Youssry . MRI Brain Image Segmentation based on Wavelet and FCM Algorithm. International Journal of Computer Applications. 47, 16 ( June 2012), 32-39. DOI=10.5120/7275-0446

@article{ 10.5120/7275-0446,
author = { Iraky Khalifa, Aliaa Youssif, Howida Youssry },
title = { MRI Brain Image Segmentation based on Wavelet and FCM Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 47 },
number = { 16 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 32-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume47/number16/7275-0446/ },
doi = { 10.5120/7275-0446 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:42:03.013014+05:30
%A Iraky Khalifa
%A Aliaa Youssif
%A Howida Youssry
%T MRI Brain Image Segmentation based on Wavelet and FCM Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 47
%N 16
%P 32-39
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image segmentation plays a preliminary and indispensable step in medical image processing. Magnetic resonance (MR) segmentation used for brain tissues extraction white matter (WM), gray matter (GM) and cerebrospinal fluids (CSF). These tissues help in many medical image segmentation applications such as radiotherapy planning, clinical diagnosis, treatment planning and Alzheimer disease. This paper presents a novel manipulation or utilization of Fuzzy C- Means (FCM) Clustering by using wavelet Decomposition for feature extraction and feature vector treat as input to FCM. This algorithm is called Wavelet Fuzzy C- means (WFCM), the algorithm results are compared with standard FCM and Kernelized Fuzzy C- Means (KFCM). The performance of the proposed segmentation algorithm provides satisfactory results compared with the other two algorithms.

References
  1. K. Xiao, S. Hock, and A. Bargiela, "Automatic brain MRI segmentation scheme based on feature weighting factors selection on fuzzy c-means clustering algorithms with Gaussian smoothing," International Journal of Computational Intelligence in Bioinformatics and Systems Biology, vol. 1, pp. 316-331, 2010.
  2. N. Sharma and L. Aggarwal, "Automated medical image segmentation techniques," Journal Medical Physics, vol. 35, pp. 3-14, 2010.
  3. J. Rogowska, "Overview and Fundamentals of Medical Image Segmentation," Handbook of Medical Imaging: Processing and Analysis Management, I. Bankman, Ed, pp. 69-85, 2009.
  4. S. Angenent, E. Pichon, and A. Tannenbaum, "Mathematical Methods in Medical Image Processing," Bulletin of the American Mathematical Society, vol. 43, pp. 365-296, 2006.
  5. S. Fiorentini, I. Larrabide, and J. Marcelo. (20/6/2010). A simple 3D image segmentation techniques over medical data. Available: http://www. frcu. utn. edu. ar/deptos/depto_3/32JAIIO/sis/sis. html
  6. K. Chuang, H. Tzeng, S. Chen, J. Wu, and J. Chen, " Fuzzy c-means clustering with spatial information for image segmentation. ," Computerized Medical Imaging and Graphics vol. 30, pp. 9-15, 2006.
  7. K. Sikka, N. Sinha, P. Singh, and A. Mishra, "A fully automated algorithm under modified FCM framework for improved brain MR image segmentation," Magnetic Resonance Imaging, vol. 27, pp. 994-1004, 2009.
  8. J. Jan, Medical Image Processing (Signal Processing and Communications): CRC Press, 2006.
  9. D. Feng, "Segmentation of Soft Tissues in Medical Images," P. h. D thesis National University of Singapore, 2005.
  10. O. wirjadi, "Survey of 3D image segmentation methods," Technical report, 2007.
  11. L. Morra, S. Delsanto, and F. Lamberti, Methods for Neural-Network-Based Segmentation of Magnetic Resonance Images: John Wiley & Sons, Inc. , 2006.
  12. D. Withey and Z. Koles, "Medical image segmentation: Methods and software," Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging, 6th International Symposium, pp. 140-143, 2007.
  13. K. Wong, "Medical Image Segmentation: Methods and Applications in Functional Imaging," Handbook of Biomedical Image Analysis, , vol. II ; Segmentation Models Part B,, pp. 111-182, 2005.
  14. J. Sijbers, P. Scheunders, M. Verhoye, V. Linden, D. V. Dyck, and E. Raman, "Watershed-based segmentation of 3D MR data for volume quantization," Journal of Magnetic Resonance Imaging, vol. 15, pp. 679-688, 1997.
  15. L. Zhong, "Stochastic Segmentation Method For Interesting Region Detection And Image Retrieval," P. h. D thesis, North Carolina University at Charlotte 2009.
  16. M. Kass, M. Witkin, and D. Terzopoulos, "Snakes: active contour models," International Journal of Vision, vol. 1, pp. 321-331, 1987.
  17. P. F. Chen, "Image Segmentation/Registration: a Variational Framework for 2-D and 3-D Applications," Ph. D thesis , North Carolina State University, 2009.
  18. A. F. Goldszal and D. L. Pham, "Volumetric Segmentation of Magnetic Resonance Images of the Brain," in Handbook of Medical Image processing, I. Bankman, Ed. , ed: Academic Press, 2000, pp. 185-194.
  19. J. Solomon, "Computer-assisted segmentation and tracking of brain lesions in magnetic resonance image based on probabilistic in space and time," M. sc thesis binghamton university, 1993.
  20. S. Luo, "Automated Medical Image Segmentation Using a New Deformable Surface Model," IJCSNS International Journal of Computer Science and Network Security, vol. 6, pp. 109-115, 2006.
  21. D. Pham, C. Xu, and J. Prince, "Current methods in medical image segmentation," Annual Review of Biomedical Engineering, pp. 315-337, 2000.
  22. D. Pham, C. Xu, and L. Prince, "A Survey of Current Methods in Medical Image Segmentation," Annual Review of Biomedical Engineering, vol. 2, pp. 315-338, 1998.
  23. J. Wang, J. Kong, L. Yinghua, Q. Miao, and B. Zhang, "A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints," Computerized Medical Imaging and Graphics, vol. 32,, pp. 685-698, 2008.
  24. C. Dunn, "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters," Journal of Cybernetics, vol. 3, pp. 32-57, 1973.
  25. J. C. Bezdek, "Pattern Recognition with Fuzzy Objective Function Algoritms," Plenum Press, New York, 1981.
  26. J. Yu and Y. Wang, "Molecular Image Segmentation Based on Improved Fuzzy Clustering," Journal of Biomedical Imaging, vol. 2007, p. 9 pages, 2008.
  27. D. L. Pham, "spatial models for fuzzy clustering. ," Computer Vision and Image Understanding vol. 84, pp. 285-97, 2001.
  28. S. Y. M Ahmed, N Mohamed, A Farag,T Moriarty "A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. ," IEEE Transaction on Medical Imaging, vol. 21, pp. 193-200. , 2002.
  29. A. Liew and H. Yan, "An adaptive spatial fuzzy clustering algorithm for 3D MR image segmentation," IEEE Transaction on Medical Imaging, vol. 22, 2003.
  30. W. Cai, S. Chen, and D. Zhang, "Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. ," Pattern Recognition, vol. 40, pp. 825-838, 2007.
  31. J. Wang, J. Kong, L. Yinghua, Q. Miao, and B. Zhang, "A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints," Computerized Medical Imaging and Graphics, vol. 32, pp. 685-698, 2008.
  32. K. Sikka, N. Sinha, K. Singh, and K. Mishra, "A fully automated algorithm under modified FCM framework for improved brain MR image segmentation " Magnetic Resonance Imaging, vol. 27, pp. 994-1004, 2009.
  33. D. -Q. Zhang and S. -C. Chen, "Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm," Neural Process. Lett. , vol. 18, pp. 155-162, 2003.
  34. D. Zhang and C. Chen, "Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure," IEEE Trans. Systems Man Cybernet, vol. 34 pp. 1907-1916, 2004.
  35. D. Zhang and C. Chen, "A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. ," Artificial Intelligence in Medicine, vol. 32, pp. 37-50, 2004.
  36. S. Kannana, A. Sathyab, S. Ramathilagam, and R. Devi, "Novel segmentation algorithm in segmenting medical images," The Journal of Systems and Software, vol. 83, pp. 2487-2495, 2010.
  37. E. Zanaty, S. Aljahdali, and N. Debnath, "Improving Fuzzy Algorithms For Automatic Magnetic Resonance Image Segmentation," Proceedings of seventeenth International Conference of Software Engineering and Data Engineering, Los Angeles, California, USA, pp. 60-66, 2008.
  38. E. Zanaty, S. Aljahdali, and N. Debnath, "A kernelized fuzzy c-means algorithm for automatic magnetic resonance image segmentation," J. Comp. Methods in Sci. and Eng. , vol. 9, pp. 123-136, 2009.
  39. E. Zanaty, S. Aljahdali, and N. Debnath, "Improving Fuzzy Algorithms for Automatic Magnetic Resonance Image Segmentation," The International Arab Journal of Information Technology, vol. 7, pp. 271-279, 2010.
  40. M. Yang and H. Tsai, "A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction," Pattern Recognition Letters, vol. 29 pp. 1713-1725, 2008.
  41. BrainWeb: Simulated Brain Database (SBD) http://brainweb. bic. mni. mcgill. ca/brainweb/.
Index Terms

Computer Science
Information Sciences

Keywords

Image Segmentation Fuzzy C-means Kernel Method Kernel-induced Distance Magnetic Resonance Imaging Wavelet