Fuzzy Edge Detection Using Fuzzy C-Means Thresholding for MRI Brain Image
Keywords:
Fuzzy logic, Fuzzy C-Means Thresholding, Fuzzy Edge detection, Fuzzy interface system, MRI head scansAbstract
In this paper, the work aimed a robust edge detection based on fuzzy technique for MRI brain image. Segmentation is the critical task in medical applications and also it is the most important task in medical image analysis. In brain image, segmentation is commonly used for analyse the brain changes and structure of the brain image and analyse the region of the brain image. Edge detection is the basic tool for segmentation. Edge detection is the finding the boundary of the particular image and edges occur on the boundary between the object and the background. Here, this paper segments the MRI image using fuzzy c-means thresholding. It covert the grey image to binary image and the result image applied fuzzy interface system and find edge of the particular object in the MRI Image. Experiments were done by using the MRI scan images
References
M.R Garey and D.S Johnson, “Computers and Intractability: A Guide to the Theory of NP-Completeness”. New York: W.H Freeman, 1979
Er Kiranpreet Kaur, Er Vikram Mutenja ,Er Inderjeet Singh Gill,” Fuzzy Logic Based Image Edge Detection Algorithm in MATLAB”, International Journal of Computer Applications (0975 – 8887), Volume 1 – No. 22, 2010.
Yasar Becerikli and Tayfun M. Karan, “A New Fuzzy Approach for Edge Detection”, Springer-Verlag Berlin Heidelberg, LNCS 3512, p 943 – 951, 2005.
Du Gen-Yuan, MianoFang, Tian Sheng-Li,Guo Xi-Rong., “Remote Sensing Image Sequence Segmentation Based On The Modified Fuzzy C-Means”, Journal Of Software , Vol.5, No. 1, pp.28-35, 2009.
Er Kiranpreet Kaur, Er Vikram Mutenja, Er Inderjeet Singh Gill, “Fuzzy Logic Based Image Edge Detection Algorithm in MATLAB”, International Journal of Computer Applications, Vol 1 – No. 22, 2010.
Suryakant, Neetu Kushwaha, “Edge Detection using Fuzzy Logic in Matlab”, International Journal of Advanced Research in Computer Scienceand Software Engineering, Vol. 2, Issue 4, April 2012.
Yau-Hwang Kuo, Chang-Shing Lee and Chao-Chin Liu, “A New Fuzzy Edge Detection Method for Image Enhancement”, IEEE,p 1069-1074 97.
N. Senthilkumaran, R. Rajesh, "Edge Detection Techniques for Image Segmentation and A Survey of Soft Computing Approaches", International Journal of Recent Trends in Engineering, Vol. 1, No. 2, PP.250-254, May 2009.
Hu L., Cheng H. D. and Zang M.” A high performance edge detector based on fuzzy inference rules”. An International Journal on Information Sciences, vol. 177,Nov 2007, no. 21, pp. 4768-4784.
Tao, C. W. et al(1993), “A Fuzzy if-then approach to edge detection”, Proc. of 2nd IEEE intl.conf. on fuzzy systems, pp. 1356–1361.
Li, W. (1997),” Recognizing white line markings for vision-guided vehicle navigation by fuzzy Reasoning”, Pattern Recognition Letters, 18: 771–780.
A. K. Jain, M. N. Murty, and P. J. Flynn, "Data clustering: a review," ACM Computing Surveys, vol.31, pp. 264-323,1999.
J. Liu and M. Xu, "Kernelized fuzzy attribute C-means clustering algorithm," Fuzzy Sets and Systems, vol. 159, pp.2428-2445, 2008.
A. B. Goktepe, S. Altun, and A. Sezer, "Soil clustering by fuzzy c-means algorithm," Advances in Engineering Software, vol. 36, pp. 691-698, 2005.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
