A Comparative Analysis of Clustering based Segmentation Algorithms in Microarray Images
Lakshmana Phaneendra Maguluri1, Keshav Rajapanthula2, P. Naga Srinivasu3

1Lakshmana phaneendra maguluri, PG Student, Department of Information and Technology, GITAM Institute Of Technology, Gitam University, Vishakapatanam, AP, India.
2KESHAV RAJAPANTHULA, PG Student, Department of IT, GIT, GU, Vishakapatanam, AP, India.
3NAGA SRINIVASU PARVATHANENI, PG Student, Department of CST, GIT, GU, Vishakapatanam, AP, India
Manuscript received on March 11, 2013. | Revised Manuscript Received on March 12, 2013. | Manuscript published on March 25, 2013. | PP: 27-32 | Volume-1 Issue-5, March 2013. | Retrieval Number: E0210031513/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: As of now, several improvements have been carried out to increase the performance of previous conventional clustering algorithms for image segmentation. However, most of them tend to have met with unsatisfactory results. In order to overcome some of the drawback like dead centers and trapped centers, in this article presents a new clustering-based segmentation technique that may be able to overcome some of the drawbacks we are passing with conventional clustering algorithms. We named this clustering algorithm as optimized kmeans clustering algorithm for image segmentation. OKM algorithm that can homogenously segment an image into regions of interest with the capability of avoiding the dead centre and trapped centre problems. The robustness of the OKM algorithm can be observed from the qualitative and quantitative analyses.
Keywords: Clustering algorithms; dead center problem; Microarray processing; Image segmentation; Microarray processing.