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Performance Evaluation of Various Segmentation Techniques on MRI of Brain Tissue

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
U. V. Suryawanshi, S. S. Chowhan, U. V. Kulkarni
10.5120/ijca2017912784

U V Suryawanshi, S S Chowhan and U V Kulkarni. Performance Evaluation of Various Segmentation Techniques on MRI of Brain Tissue. International Journal of Computer Applications 157(8):12-20, January 2017. BibTeX

@article{10.5120/ijca2017912784,
	author = {U. V. Suryawanshi and S. S. Chowhan and U. V. Kulkarni},
	title = {Performance Evaluation of Various Segmentation Techniques on MRI of Brain Tissue},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2017},
	volume = {157},
	number = {8},
	month = {Jan},
	year = {2017},
	issn = {0975-8887},
	pages = {12-20},
	numpages = {9},
	url = {http://www.ijcaonline.org/archives/volume157/number8/26850-2017912784},
	doi = {10.5120/ijca2017912784},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Accuracy of segmentation methods is of great importance in brain image analysis. Tissue classification in Magnetic Resonance brain images (MRI) is an important issue in the analysis of several brain dementias. This paper reviews the performance of segmentation techniques that are used on Brain MRI. A large variety of algorithms for segmentation of Brain MRI have been developed. This paper aims at to study the performance segmentation process on MR images of the human brain, using Fuzzy c-means (FCM), Kernel based Fuzzy c-means clustering (KFCM), Spatial Fuzzy c-means (SFCM) and Improved Fuzzy c-means (IFCM). The review covers imaging modalities, MRI and methods for noise reduction and segmentation approaches. After applying all methods on MRI brain images, which are degraded by salt-pepper noise, it is demonstrated that the IFCM algorithm performs more robust to noise than the standard FCM algorithm. We conclude with the trend of future research in brain segmentation by changing norms in FCM, for better results.

References

  1. D. Tian, L. Fan (2007), “A brain MR images segmentation method based on SOM neural network,” In: The 1st Int. Conf. bioinformatics and biomedical engineering, pp 686–689.
  2. X. Han, B. Fischl (2007), “Atlas renormalization for improved brain MR image segmentation across scanner platforms,” IEEE Trans. Med. Imag., 26(4):479–486.
  3. D. L. Pham, C. Y. Xu, J. L. Prince, “A survey of current methods in medical image segmentation,” Ann Rev. Biomed. Eng., 2 (2000), pp.315–37 [Technical report version, JHU/ECE 99-01, Johns Hopkins University].
  4. M. N. Ahmed, S. M. Yamany, N. Mohamed, A. A. Farag, and T. Moriarty (2002), “A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data,” IEEE Trans. on Med. Imag., 21(3), pp.193–199.
  5. L. Ma and R. C. Staunton (2007), “A modified fuzzy c-means image segmentation algorithm for use with uneven illumination Patterns,” Pattern Recognition, 40(11), pp.3005–3011.
  6. S. Shen, W. A. Sandham, M. H. Granat, and A. Sterr (2005), “MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization”. IEEE Trans. On Information Technology in Biomedicine, vol. 9, no. 3, September 2005 459.
  7. K. S. Chuang, H. L. Hzeng, S. Chen, J. Wu, T. J. Chen, (2006), “Fuzzy c-means clustering with spatial information for image segmentation,” Comp. Med. Imag. and Graphics, 30, pp.9-15.
  8. C. L. Li, D. B. Goldgof, L. O. Hal, “Knowledge-based Classification and tissue labeling of MR images of human brain,” IEEE Trans. Med. Imag., vol. 12, no. 4, pp. 740–750, Apr. 1993.
  9. L. O. Hall, A. M. Bensaid, L. P. Clarke, R. P. Velthuizen, M. S. Silbiger, J. C. Bezdek , “A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain,” IEEE Trans. Neural Netw., vol. 3, no. 5, pp. 672–682, Sep. 1992.
  10. B. Alfano, A. Brunetti, et al. “Unsupervised, automated segmentation of the normal brain using multispectral relaxometric magnetic resonance approach,” Magn. Reson. Med., Vol. 37, 1997, pp. 84-93.
  11. N. Guillermo, Abras and Virginia L. Ballarin, “A Weighted K-means Algorithm applied to Brain Tissue Classification”.
  12. R. De La Paz, E. Herskovits, V. Gesu, et al. “Cluster analysis of medical magnetic resonance images MRI data: Diagnostic application and evaluation,” SPIE Extracting Meaning from Complex Data: Processing, Display, Interaction, Vol. 1259, 1990, pp. 176 –181.
  13. Y. Wei, J. Fritts, and S. Fangting, “A hierarchical image segmentation algorithm,” ICME '02. Proc. IEEE Int. Conf., Vol. 2, 2002, pp. 221-224.
  14. J. C. DUNN, “A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters,” J. Cybern., vol. 3, pp. 32–57, 1973.
  15. J. C. BEZDEK, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York 1981.
  16. D. Q. Zhang and S. 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.
  17. D. Q. Zhang, “Kernel-based fuzzy clustering incorporating spatial constraints for image segmentation,” in Proc. of the 2nd Int. Conf. on Mach. Lear. And Cyb., pp. 2189-2192 Vol. 4, 2003.
  18. R. Pohle and K. D. Toennies, “Segmentation of medical images using adaptive region growing,” Proc. SPIE— Med. Imag., vol. 4322, pp. 1337–1346, 2001.
  19. Brain Web [Online]. Available: ww.bic.mni.mcgill.ca/brainweb/
  20. Yong Yang, “Image Segmentation by Fuzzy C-Means Clustering Algorithm with a Novel Penalty Term,” Computing and Informatics, Vol. 26, 2007, 17–31
  21. Keh-Shi Chuang, Hong-Long Tzeng, Sharon Chen, Jay Wu, and Tzong-Jer Chen, “Fuzzy c-means clustering with spatial information for image segmentation,” Comput. Med. Imag. and Graphics 30 (2006) 9–15.
  22. Aimin Yang, Lingmin Jiang, Yongmei Zhou, “A KFCM-based Fuzzy Classifier”.
  23. Xiao-li Jin, Tu-sheng Lin Liang Liao and Teng Wang, “Multi-Spectral MRI Brain Image Segmentation Based on Kernel Clustering Analysis,” 2012 Int. Conf. on System Eng. and Modeling (ICSEM 2012) IPCSIT vol. 34 (2012).
  24. Maryam Rastgarpour and Jamshid Shanbehzadeh, “A New Kernel-Based Fuzzy Level Set Method for Automated Segmentation of Medical Images in the Presence of Intensity In homogeneity”.
  25. D. L. Pham, J. L. Prince, “An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities,” Pattern Recognit Lett 1999; 20(1):57—68.
  26. Y. A. Tolias, S. M. Panas, “On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system,” IEEE Signal Proc. Lett., 1998; 5(10):245—7.
  27. Y. A. Tolias, S. M. Panas, “Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions,” IEEE Trans. Syst, Man., Cybernet Part A 1998; 28(3):359—69.
  28. AWC. Liew, S. H. Leung, W. H. Lau, “Fuzzy image clustering incorporating spatial continuity,” IEE Proc. Vis. Image Signal Proc. 2000; 147(2):185—92.

Keywords

Image Segmentation, Preprocessing, MRI, FCM, KFCM, SFCM and IFCM