Nature Inspired Computing pp 75-82 | Cite as
Segmentation of Mammograms Using a Novel Intuitionistic Possibilistic Fuzzy C-Mean Clustering Algorithm
Abstract
There is a partitioning of a data set X into c-clusters in clustering analysis. In 1984, fuzzy c-mean clustering was proposed. Later, fuzzy c-mean was used for the segmentation of medical images. Many researchers work to improve the fuzzy c-mean models. In our paper, we proposed a novel intuitionistic possibilistic fuzzy c-mean algorithm. Possibilistic fuzzy c-mean and intuitionistic fuzzy c-mean are hybridized to overcome the problems of fuzzy c-mean. This proposed clustering approach holds the positive points of possibilistic fuzzy c-mean that will overcome the coincident cluster problem, reduces the noise and brings less sensitivity to an outlier. Another approach of intuitionistic fuzzy c-mean improves the basics of fuzzy c-mean by using intuitionistic fuzzy sets. Our proposed intuitionistic possibilistic fuzzy c-mean technique has been applied to the clustering of the mammogram images for breast cancer detector of abnormal images. The experiments result in high accuracy with clustering and breast cancer detection.
Keywords
Intuitionistic fuzzy c-mean Possibilistic c-mean Membership degree Non-membership degree Hesitation degreeReferences
- 1.Otsu, N.: A threshold selection method from gray level histograms. IEEE Trans. Syst. Man Cyber. 9, 62–66 (1979)Google Scholar
- 2.Krishnapuram, R., Keller, J.M.: A possibilistic approach of clustering model. IEEE Trans. Fuzzy Syst. 1, 98–110 (1993)CrossRefGoogle Scholar
- 3.Pal, N.R., Pal, K., Bezdek, J. C.: A mixed c-means clustering model. Interrnational Conference Fuzzy Systems, Spain, pp. 11–21 (1997)Google Scholar
- 4.Pal, N.R., Pal, K., Keller, J. M., Bezdek, J.C.: A possibilistic fuzzy c-mean clustering algorithm. 13 (2005)Google Scholar
- 5.de Carvalho, F.D.A.: Fuzzy c-mean clustering for symbolic interval data. Pattern Recogn. Lett. (2006)Google Scholar
- 6.Ji, Z.X., Sun, Q.S., Xia, D.S.: A modified possibilistic fuzzy c-means clustering algorithm for bias MR images. Comput. Med. Imaging Graph. 35, 383–397 (2011)Google Scholar
- 7.Treerattanapitak, K., Juruskulchai, C.: Outlier Detection with possibilistic exponential fuzzy clustering. 8th FSKD proceeding, pp. 453–457 (2011)Google Scholar
- 8.Wahid, A., Gao, X., Andreae, P.: Multi-view clustering of web documents using multi-objective genetic algorithm. IEEE Congress on Evolutionary Computation (2014)Google Scholar
- 9.Attanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20, 87–96 (1986)CrossRefGoogle Scholar
- 10.Chaira, T.: A novel intuitionistic fuzzy c-means clustering algorithm and its application to medical images. Appl. Soft. Comput. 11, 1711–1717 (2011)CrossRefGoogle Scholar
- 11.MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium Mathematical Statistics and Probability. vol. 1, pp. 281–297 (1966)Google Scholar
- 12.Treerattanapitak, K., Juruskulchai, C.: Possibilistic exponential fuzzy clustering. J. Comput. Sci. Technol. 28, 311–322 (2013)MathSciNetCrossRefMATHGoogle Scholar