A Comparative Analysis of K-Means and Fuzzy C-Means Clustering Algorithms Based on CT Liver Image
Main Article Content
Image processing techniques are broadly used in different areas of medical imaging to detect different types of abnormalities. The clustering algorithm is used in image processing for image segmentation. Image processing technique can help to detect the tumor and also it helps to identify the affected parts of the organs. This paper describes two clustering algorithm K-Means and Fuzzy C-Means clustering to compare their performance based on CT liver image. The segmentation result of K-Means is compared to the segmentation result of Fuzzy C-Means clustering. Experiments were conducted to evaluate their performance based on some criteria such as computational time, energy, homogeneity, PSNR etc.
How to Cite
, M. S. I. M. M. H. M. R. M. M. J. A. S. “A Comparative Analysis of K-Means and Fuzzy C-Means Clustering Algorithms Based on CT Liver Image”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 6, no. 2, Feb. 2018, pp. 92-95, doi:10.17762/ijritcc.v6i2.1428.