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Medical Image Segmentation using Modified K Means Clustering

by Kalpana Shrivastava, Neelesh Gupta, Neetu Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 103 - Number 16
Year of Publication: 2014
Authors: Kalpana Shrivastava, Neelesh Gupta, Neetu Sharma
10.5120/18157-9341

Kalpana Shrivastava, Neelesh Gupta, Neetu Sharma . Medical Image Segmentation using Modified K Means Clustering. International Journal of Computer Applications. 103, 16 ( October 2014), 12-16. DOI=10.5120/18157-9341

@article{ 10.5120/18157-9341,
author = { Kalpana Shrivastava, Neelesh Gupta, Neetu Sharma },
title = { Medical Image Segmentation using Modified K Means Clustering },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 103 },
number = { 16 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 12-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume103/number16/18157-9341/ },
doi = { 10.5120/18157-9341 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:34:43.281278+05:30
%A Kalpana Shrivastava
%A Neelesh Gupta
%A Neetu Sharma
%T Medical Image Segmentation using Modified K Means Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 103
%N 16
%P 12-16
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image segmentation is an important technique for image processing which aims at partitioning the image into different homogeneous regions or clusters. Lots of general-purpose techniques and algorithms have been developed and widely applied in various application areas. For the study of anatomical structures and to identify the region of interest. Magnetic Resonance Images are used to produce images of soft tissue of human body. Noise present in the Brain MRI images are multiplicative noise and reductions of these noise are difficult task. However, accurate Segmentation of the MRI images is very important and crucial for the exact diagnosis by computer aided clinical tools. A large variety of algorithms for segmentation of MRI images had been developed. However most of these have some limitations, to overcome these limitations; modified k means clustering is proposed. The comparison of existing segmentation approaches such as C-Means Clustering, K-Means Clustering with Modified K-Means Clustering is performed then the performance evaluated. Finally generated outcomes of the Fuzzy c- means clustering, k-means clustering and modified k means clustering algorithm for the brain MRI shows that modified k-means clustering technique gives better results for all performance measuring parameters such as structural similarity index measure, structural content, mean squared error and peak to signal noise ratio.

References
  1. M. Masroor Ahmed, Dzulkifli Bin Mohamad, "Segmentation of Brain MR Images for Tumor and combining by k means clustering International Journal of Image Processing, vol. 2 , no. 1, pp 27-34,2008
  2. U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy, Advances in Knowledge Discovery and Data Mining,AAAI/MIT Press (1996)
  3. M. N. Murty, A. K. Jain, P. J. Flynn, and Data clustering: a review, ACM Computes. Survey. 31(3) (1999) 264-323
  4. A. K. Jain, R. C. Dubes, Algorithms for Clustering Data,Prentice Hall, Englewood Cliffs, NJ(1988)
  5. R. T. Ng, J. Han, Efficient and effective clustering methods for spatial data mining, in: Proceedings of the Twentieth International Conference on Very Large Databases, Santiago, Chile(1994) 144-155620
  6. Xiuyun Li, Jie Yang, Qing Wang, Jinjin Fan, Pang Liu,2010 Research and Application of Improved K-means Algorithm Based on Fuzzy Feature Selection.
  7. Shehroz S. Khan, Amir Ahmad,Cluster center initialization algorithm for K-means clustering, Pattern Recognition Letters 25 (2004) 1293-1302
  8. Bradley, P. S. , Fayyad, U. M. , Refining initial points for K Means clustering. In: Sharlik, J. (Ed. ), Proc. 15th Internat. Conf. on Machine Learning (ICML98). Morgan Kaufmann, San Francisco, CA,(1998) 91-99 621
  9. K. S. Ravichandran and B. Acanthi," Color Skin Segmentation Using K-Means" Cluster International Journal of Computational and Applied Mathematics ISSN 1819-4966 Volume 4 Number 2 (2009), pp. 153–157
  10. Jude hemanth. D, D. Selvathi and J. Anitha,"Effective Fuzzy Clustering Algorithm for Abnormal MR Brain Image Segmentation", International/Advance Computing Conference (IACC 2009), IEEE, 2009.
  11. Sorin Istrail, "An Overview of Clustering Methods", With Applications to Bioinformatics.
  12. Dzung L. Pham, ChenyangXu, and Jer y L. Prince Current Methods In Medical Image Segmentation Department of Electrical and Computer Engine ring, The Johns Hopkins University, An u. Rev. Biomed. Eng. 20 0. 02:315ñ37
  13. Zhou Wang, Alan Conrad Bovik. , et. al. Image Quality Assessment: From Error Visibility to Structural Similarity? IEEE Transactions on Image processing, vol. 13, no. 4, April 2004.
  14. Ahmet M. Eskicioglu and Paul S. Fisher Image Quality Measures and their performance? IEEE Transactions on Communications, Vol. 43, No. 12, December 1995.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy C-Means K-Means SSIM SC PSNR

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