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Performance Improvement of Fuzzy C-mean Algorithm for Tumor Extraction in MR Brain Images

by Neelofar Sohi, Lakhwinder Kaur, Savita Gupta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 59 - Number 5
Year of Publication: 2012
Authors: Neelofar Sohi, Lakhwinder Kaur, Savita Gupta
10.5120/9547-4000

Neelofar Sohi, Lakhwinder Kaur, Savita Gupta . Performance Improvement of Fuzzy C-mean Algorithm for Tumor Extraction in MR Brain Images. International Journal of Computer Applications. 59, 5 ( December 2012), 40-45. DOI=10.5120/9547-4000

@article{ 10.5120/9547-4000,
author = { Neelofar Sohi, Lakhwinder Kaur, Savita Gupta },
title = { Performance Improvement of Fuzzy C-mean Algorithm for Tumor Extraction in MR Brain Images },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 59 },
number = { 5 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 40-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume59/number5/9547-4000/ },
doi = { 10.5120/9547-4000 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:05:23.296633+05:30
%A Neelofar Sohi
%A Lakhwinder Kaur
%A Savita Gupta
%T Performance Improvement of Fuzzy C-mean Algorithm for Tumor Extraction in MR Brain Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 59
%N 5
%P 40-45
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Aim of this paper is to develop an efficient fuzzy c-mean based segmentation algorithm to extract tumor region from MR brain images. First, cluster centroids are initialized through data analysis of tumor region, which optimizes the standard fuzzy c-mean algorithm. Next, reconstruction based morphological operations are applied to enhance its performance for brain tumor extraction. The results show that simple fuzzy c-mean could not segment the region of interest properly, whereas enhanced algorithm effectively extracts the tumor region. From comparison with existing segmentation methods, enhanced fuzzy c-mean algorithm emerges as the most effective algorithm for extracting region of interest.

References
  1. Gajanayake, Randike, Yapa, Roshan Dharshana, Hewavithana and Badra. 2009. Comparison of standard image segmentation methods for segmentation of brain tumors from 2D MR images. In Proc. IEEE 4th International Conference on Industrial and Information Systems, ICIIS'09, pp. 301-305.
  2. M. Rastgarpour and J. Shanbehzadeh. 2011. Application of AI Techniques in Medical Image Segmentation and Novel Categorization of Available Methods and Tools. International MultiConference of engineers and computer scientists, IMECS'11, Vol. 1.
  3. A. Halder and D. K. Kole. 2012. Automatic Brain Tumor Detection and Isolation of Tumor Cells from MRI Images. International Journal of Computer Applications, Vol. 39, No. 2.
  4. J. Bezdek, L. Hall and L. Clarke. 1993. Review of MR image segmentation using pattern recognition. Journal of Medical Physics, Vol. 20, pp. 1033–1048.
  5. J. K. Udupa, L. Wei, S. Samarasekera, Y. Miki, M. A. van Buchem and R. I. Grossman. 1997. Multiple sclerosis lesion quantification using fuzzy-connectedness principles. IEEE Transactions on Medical Imaging, Vol. 16, pp. 598-609.
  6. D. L. Pham. 2003. Unsupervised Tissue Classification in Medical Images using Edge-Adaptive Clustering. In Proc. 25th Annual International Conference of the IEEE EMBS, Mexico.
  7. I. Soesanti, A. Susanto, T. S. Widodo and M. Tjokronagoro. 2011. MRI Brain Images Segmentation Based on Optimized Fuzzy Logic and Spatial Information. International Journal of Video & Image Processing and Network Security, IJVIPNS-IJENS, Vol. 11, No. 4.
  8. D. L. Pham and J. L. Prince. 1999. Adaptive fuzzy segmentation of magnetic resonance images. IEEE Transactions on Medical Imaging, Vol. 18, No. 9, pp. 737-752.
  9. D. L. Pham and J. L. Prince. 1999. An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneties. Pattern Recognition Letter, Vol. 20, No. 1, pp. 57-68.
  10. M. N. Ahmed and S. M. Yamany. 2002. A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data. IEEE Transactions on Medical Imaging, Vol. 21, No. 3, pp. 193-199.
  11. S. Murugavalli and V. Rajamani. 2007. An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Neuro Fuzzy Technique. Journal of Computer Science, Vol. 3, No. 11.
  12. M. C. Clark, L. O. Hall, D. B. Goldgof, R. Velthuizen, F. R. Murtaugh and M. S. Silbiger. Unsupervised Brain Tumor Segmentation Using Knowledge-based and Fuzzy Techniques.
  13. S. Albayrak and F. Amasyali. 2003. Fuzzy C-Means Clustering on Medical Diagnosis Systems. International 12th Turkish Symposium on Artificial Intelligence and Neural Networks, TAINN' 03.
  14. L. Jiang and W. Yang. 2003. A Modified Fuzzy C-Means Algorithm for Segmentation of Magnetic Resonance Images. In Proc. 7th Digital Image Computing: Techniques and Applications, Sydney.
  15. C. Xu. ,D. L Pham and J. L. Prince. 1997. Finding the brain cortex using fuzzy segmentation, isosurfaces and deformable surfaces. In Proc. XVth International Conference on Information Processing in Medical Imaging, IPMI, pp. 399-404.
  16. S. R. Kannan. 2005. Segmentation of MRI Using New Unsupervised Fuzzy C Mean Algorithm. ICGST-GVIP Journal, Vol. 5, No. 2.
  17. K. S. Chuang, H. L. Tzeng, S. Chen. , J. Wu. and T. J. Chen. 2006. Fuzzy C-Means Clustering with Spatial Information for Image Segmentation. Computerized Medical Imaging and Graphics, Vol. 30, pp. 9-15.
  18. B. Cherradi, O. Bouattane, M. Youssfi and A. Raihani. 2011. Brain Extraction and Fuzzy Tissue Segmentation in Cerebral 2D T1-Weigthed Magnetic Resonance Images. International Journal of Computer Science Issues, Vol. 8, No. 3, In Press.
  19. D. Mortazavi, A. Z. Kouzani and H. S. Zadeh. 2012. A 3S Multi-level Thresholding Technique for Intracranial Segmentation from Brain MRI Images. Journal of Bioengineering & Biomedical Science, Vol. 2, No. 1.
  20. S. K. S. Fan and Y. Lin. 2007. A multi-level thresholding approach using a hybrid optimal estimation algorithm. Pattern Recognition Letter, Vol. 28, pp. 662-669.
  21. P. S. Liao, T. S. Chen and P. C. Chung. 2001. A fast algorithm for multi-level thresholding. Journal of Inf. Sci. Engg. , Vol. 17, pp. 713-727.
  22. D. Y. Huang and C. H. Wang. 2009. Optimal multi-level thresholding using a two-stage Otsu optimization approach. Pattern Recognition Letter, Vol. 30, pp. 275-284.
  23. R. Al-Attas and A. El-Zaart. 2007. Thresholding of medical images using minimum cross entropy. In Proc. IFMBE, Vol. 15, pp. 296-299.
  24. H. P. Ng, S. H. Ong, K. W. C. Foong, P. S. Goh and W. L. Nowinski. 2006. Medical Image Segmentation using K-Means clustering and Improved Watershed Algorithm. IEEE.
  25. C. W. Chen, J. Luo and K. J. Parker. 1998. Image segmentation via adaptive K-mean clustering and knowledge based morphological operations with biomedical applications. IEEE Transactions on Image Processing, Vol. 7, No. 12, pp 1673-1683.
  26. L. Kaur and G. Kaur. 2011. Comparison of Foreground Marker control with Watershed Segmentation Algorithms for Tumor Detection in 2D MR Images. Elsevier Journal of Digital signal processing, submitted.
  27. T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman and A. Y. Wu . 2002. An efficient k-means clustering algorithm: analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, pp. 881-892.
  28. V. Grau, A. U. J. Mewes, M. Alcañiz, R. Kikinis and S. K. Warfield. 2004. Improved Watershed Transform for Medical Image Segmentation Using Prior Information. IEEE Transactions on Medical Imaging, Vol. 23, No. 4.
  29. K. Parvati, B. S. P. Rao and M. M. Das. 2008. Image Segmentation Using Gray-Scale Morphology and Marker-Controlled Watershed Transformation. Hindawi Publishing Corporation Discrete Dynamics in Nature and Society, Vol. 2008, Article ID 384346.
  30. Z. Yu, Y. Zhao and X. F. Wang. 2008. Research Advances and Prospects of Mathematical Morphology in Image Processing," IEEE.
  31. L. Vincent. 1993. Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Transactions on Image Processing, Vol. 2, No. 2, pp. 176–201.
  32. C. W. Chen, J. Luo and K. J. Parker. 1998. Image segmentation via adaptive K-mean clustering and knowledge based morphological operations with biomedical applications. IEEE Transactions on Image Processing, Vol. 7, No. 12, pp 1673-1683.
  33. Gui, Lisowski, Faundez, Huppi, Lazeyras and Kocher. 2011. Automatic segmentation of newborn brain MRI using mathematical morphology. In Proc. IEEE International Symposium on Biomedical Imaging: FromNanotoMacro.
  34. P. V. Ingole and K. D. Kulat. 2011. A Morphological Segmentation Based Features for Brain MRI Retrieval. In Proc. 4th International Conference on Emerging Trends in Engineering and Technology, ICETET'11.
  35. D. Selvaraj and R. Dhanasekaran. 2010. Segmenting Internal Brain Nuclei in MRI Brain Image Using Morphological Operators. In Proc. International Conference on Computational Intelligence and Software Engineering, CiSE'10.
  36. Kharrat. 2009. Detection of brain tumor in medical images. In Proc. 3rd International Conference on Signals, Circuits and Systems, SCS'09.
  37. P. Dokladal. 2001. Segmentation of 3D head MR images using morphological reconstruction under constraints and automatic selection of markers. In Proc. International conference on Image Processing.
  38. L. Singh, R. B. Dubey, Z. A. Jaffery and Z. Zaheeruddin. 2009. Segmentation and Characterization of Brain Tumor from MR Images. In Proc. International conference on Advances in Recent Technologies in Communication and Computing, ARTCom'09.
  39. http://www. mathworks. com/matlabcentral/fileexchange/25532-fuzzy-c-means-segmentation
  40. J. C. Dunn. 1973. A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters. Journal of Cybernetics, Vol. 3, No. 3, pp. 32–57.
  41. J. C. Bezdek. 1981. Pattern Recognition with Fuzzy Objective Function Algorithms, New York: Plenum Press.
  42. http://www. cnblogs. com/nktblog/archive/2012/05/08/2489604. html
  43. M. Sezgin. 2004. Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging. Vol. 13, pp. 146-165.
  44. N. R. Pal and S. K. Pal. 1993. A Review on image segmentation techniques. Pattern Recognition Letter, Vol. 26, No. 9, pp. 1277-1294.
  45. N. Otsu. 1979. A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, pp. 62-66.
  46. http://www. mathworks. com/matlabcentral/fileexchange/30740-k-means-image-segmentation
  47. A. K. Bhogal, N. Singla and M. Kaur. 2010. Color Image Segmentation using k-means clustering algorithm. International Journal on Emerging Technologies, Vol. 1, No. 2, pp. 18-20.
  48. N. Sohi, L. Kaur and S. Gupta. 2012. Enhanced Thresholding algorithm to Extract Tumor region from MR brain images. Proceedings of International Conference on Electrical engineering and Computer Science.
  49. R. Kaur, L. Kaur and S. Gupta. 2011. Enhanced K-Mean Clustering Algorithm for Liver Image Segmentation to Extract Cyst Region. International Journal of Computer Applications, Special Issue on Novel Aspects of Digital Imaging Applications (DIA), Vol. 1, pp. 59–66.
  50. MATLAB statistics toolbox.
  51. S. Gupta, L. Kaur, R. C. Chauhan and S. C. saxena. 2007. A versatile technique for visual enhancement of medical ultrasound images. Digital Signal Processing, Vol. 17, pp. 542-560.
Index Terms

Computer Science
Information Sciences

Keywords

segmentation brain tumor extraction thresholding fuzzy c-mean k-mean morphology markers

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