International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 4 | Issue 5 | Year 2013 | Article Id. IJCTT-V4I5P55 | DOI : https://doi.org/10.14445/22312803/IJCTT-V4I5P55

Image segmentation using K-mean clustering for finding tumor in medical application

Piyush M. Patel, Brijesh N Shah, Vandana Shah

Citation :

Piyush M. Patel, Brijesh N Shah, Vandana Shah, "Image segmentation using K-mean clustering for finding tumor in medical application," International Journal of Computer Trends and Technology (IJCTT), vol. 4, no. 5, pp. 1239-1242, 2013. Crossref, https://doi.org/10.14445/22312803/IJCTT-V4I5P55

Abstract

Clustering algorithms have successfully been applied as a digital image segmentation technique in various fields and applications. However, those clustering algorithms are only applicable for specific images such as medical images, microscopic images etc. In this paper, we present a new clustering algorithm called Image segmentation using K-mean clustering for finding tumor in medical application which could be applied on general images and/or specific images (i.e., medical and microscopic images), captured using MRI, CT scan, etc. The algorithm employs the concepts of fuzziness and belongingness to provide a better and more adaptive clustering process as compared to several conventional clustering algorithms.

Keywords

clustering methods; K-means segmentation; partitioning methods; hierarchical methods; density-based methods; centroid method.

References

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