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Image Segmentation using Rough Set based Fuzzy K-means Algorithm

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 74 - Number 14
Year of Publication: 2013
E. Venkateswara Reddy
E. S. Reddy

Venkateswara E Reddy and E S Reddy. Article: Image Segmentation using Rough Set based Fuzzy K-means Algorithm. International Journal of Computer Applications 74(14):36-40, July 2013. Full text available. BibTeX

	author = {E. Venkateswara Reddy and E. S. Reddy},
	title = {Article: Image Segmentation using Rough Set based Fuzzy K-means Algorithm},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {74},
	number = {14},
	pages = {36-40},
	month = {July},
	note = {Full text available}


Image segmentation is critical for many computer vision and information retrieval systems, and has received significant attention from industry and academia over last three decades. Despite notable advances in the area, there is no standard technique for selecting a segmentation algorithm to use in a particular application, nor even is there an agreed upon means of comparing the performance of one method with another. This paper, explores Rough-Fuzzy K-means (RFKM) algorithm, a new intelligent technique used to discover data dependencies, data reduction, approximate set classification, and rule induction from image databases. Rough sets offer an effective approach of managing uncertainties and also used for image segmentation, feature identification, dimensionality reduction, and pattern classification. The proposed algorithm is based on a modified K-means clustering using rough set theory (RFKM) for image segmentation, which is further divided into two parts. Primarily the cluster centers are determined and then in the next phase they are reduced using Rough set theory (RST). K-means clustering algorithm is then applied on the reduced and optimized set of cluster centers with the purpose of segmentation of the images. The existing clustering algorithms require initialization of cluster centers whereas the proposed scheme does not require any such prior information to partition the exact regions. Experimental results show that the proposed method perform well and improve the segmentation results in the vague areas of the image.


  • Russo F. Edge detection in noisy images using fuzzy reasoning, IEEE transactions on instrumentation and measurement, vol. 47, no. 5 1998, pp. 1102-1105.
  • HT Farrah Wong, Nagarajan Ramachandran et al. An image segmentation method using fuzzy-based threshold, International symposium on signal processing and its applications (ISSPA), August (2001), pp. 144-147.
  • Borji A. and Hamidi M. Evolving a fuzzy rule base for image segmentation, Proceedings of world academy of science, engineering and technology, vol. 22, July 2007, pp. 4-9
  • A. Rakhlin and A. Caponnetto, "Stability of K-Means clustering", Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA, 2007, pp. 216–222.
  • . A. Rui and J. M. C. Sousa, "Comparison of fuzzy clustering algorithms for Classification", International Symposium on Evolving Fuzzy Systems, 2006 , pp. 112-117.
  • V. S. Rao and Dr. S. Vidyavathi, "Comparative Investigations and Performance Analysis of FCM and MFPCM Algorithms on Iris data", Indian Journal of Computer Science and Engineering, vol. 1, no. 2, 2010 pp. 145-151.
  • Pawlak, Z. (1982). Rough sets. Internat. J. Comput. Inform. Sci. , 11, 341–356
  • Pawlak, Z. (1991). Rough sets: Theoretical aspects of reasoning about data, vol. 9 of System Theory, Knowledge Engineering and Problem Solving. Dordrecht: Kluwer.
  • Y. Yong, Z. Chongxun and L. Pan, "A Novel Fuzzy C-Means Clustering Algorithm for Image Thresholding", Measurement Science Review, vol. 4, no. 1, 2004.
  • K. Nirulata, S. Meher, "Skin Tumor Segmentation using Fuzzy c-means Clustering with Neighbourhood Attraction", Communicated to International Journal of Computers and Electrical Engineering.
  • X. Hui, J. Wu and C. Jian, "K-Means clustering versus validation measures: A data distribution perspective", IEEE Transactions on Systems, Man, and cybernetics, vol. 39, Issue-2, 2009 , pp. 319-331.