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  Enhancing Spatial FCM using Intuitionistic Fuzzy Sets  
  Authors : Prabhjot Kaur
  Cite as:

 

A conventional fuzzy c- means (FCM) clustering algorithm did not use the spatial information of the data and is very much sensitive to noise. To improve the noise sensitivity of FCM, Spatial FCM (SFCM) incorporates the spatial information to improve the results. Intuitionistic fuzzy sets introduce hesitation factor in the fuzzy sets to enhance the performance of fuzzy sets and also added entropy to maximize the good points in data. This paper proposed a variant of SFCM by using intuitionistic fuzzy sets. The algorithm is tested on the CT scan images and after comparison it is observed that SIFCM outperformed SFCM and IFCM in case of images.

 

Published In : IJCSN Journal Volume 5, Issue 1

Date of Publication : February 2016

Pages : 55-59

Figures :02

Tables : 02

Publication Link : Enhancing Spatial FCM using Intuitionistic Fuzzy Sets

 

 

 

Prabhjot Kaur : is working as a Reader in maharaja Surajmal Institute of Technology. She has done her Ph.D. in Computer Science. She is the member of IEEE, CSI and ISTE.

 

 

 

 

 

 

 

Intuitionistic Fuzzy Set

Hesitation Degree

Fuzzy Clustering

Intuitionistic Fuzzy Generator

Spatial Information

Image Segmentation

FCM

This paper proposed an intuitionistic approach of clustering by adding the spatial information into IFCM. It combined the advantages of IFCM and SFCM to enhancethe image segmentation process. The algorithm is tested with CT brain scan image and from the results, it is clear that SIFCM gives better results compared to other two methods.

 

 

 

 

 

 

 

 

 

[1] K.T. Atanassov’s, Intuitionistic fuzzy sets, VII ITKR’s Session, Sofia, 983 (Deposed in Central Science – Technology Library of Bulgaria Academy of Science – 1697/84). [2] K.T. Atanassov, Intuitionistic Fuzzy Sets Theory and Applications Series in Fuzzinessand Soft Computing, Phisica-Verlag, 1999. [3] J.C. Bezdek, L.O. Hall, L.P. Clark, Review ofMRsegmentation technique in patternrecognition, Medical Physics 10 (20) (1993) 33–48. [4] T. Chaira, A.K. Ray, O. Salvetti, Intuitionistic fuzzy c means clustering in medical image segmentation, in: Proc. of ICAPR, ISI Calcutta, India, 2007. [5] L.A. Zadeh, Fuzzy sets and systems, in: Proc of Symposium on systems theory, Polytechnic Institute of Brooklyn, NY, USA, 1965. [6] A. Kaufmann, Introduction to the Theory of Fuzzy Subsets: Fundamental Theoretical Elements-1, Academic Press, New York, 1980. [7] R.R. Yager, On the measures of fuzziness and negation part II lattices, Information and Control 44 (1980) 236– 260. [8] Pedrycz W, Waletzky J. Fuzzy clustering with partial supervision. IEEE Trans Syst Man Cybern Part B Cybern 1997;27:787–95. [9] Ahmed MN, Yamany SM, Mohamed N, Farag AA, Moriarty T. Amodified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 2002;21:193–9. [10] Chuang Keh-Shih, Tzeng Hong-Long, Chen Sharon et al.(2006),“Fuzzy C-means clustering with spatial information for image segmentation,”,Computeried Medical Imaging and graphics, 30(2006) 9-15. [11] Masulli, F., Schenone, A., 1999. A fuzzy clustering based segmentation system as support to diagnosis in medical imaging. Artif. Intell. Med. 16 (2), 129–147.