Next Article in Journal
Effects of Joint Replenishment Policy on Company Cost under Permissible Delay in Payments
Previous Article in Journal
Calculation of Generalized Level Densities for Nuclei in Mass Region 20 < A < 50
Article Menu

Article Versions

Export Article

Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Issue were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on as a courtesy and upon agreement with the previous journal publisher.
Open AccessArticle
Math. Comput. Appl. 2010, 15(2), 240-247;

Microarray Image Segmentation Using Clustering Methods

Department of Computer Engineering, Fatih University, 34500, B.Çekmece, Istanbul, Turkey
Authors to whom correspondence should be addressed.
Published: 1 August 2010
PDF [978 KB, uploaded 4 April 2016]


Microarray image processing is a technology for viewing and computationally measuring thousands of genes at the same time. Gene expressions provide information about the cell activity in an organism. Observing a substantial change in gene expressions between the cDNA (complementary DNA) microarray experiments of an organism can be a sign of a disease. The goal of this study is to make a fine distinction against the gene expressions in the microarray image processing. For this reason, two clustering methods have been experimented and compared. In this study we have specifically investigated the segmentation step of the microarray image. Other than the segmentation methods used in commercial packages we have used the clustering techniques. We have applied fuzzy c–means and k-means methods and observed the results.
Keywords: Microarray Image; Image Segmentation; Clustering Microarray Image; Image Segmentation; Clustering
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

MDPI and ACS Style

Uslan, V.; Bucak, I.Ö. Microarray Image Segmentation Using Clustering Methods. Math. Comput. Appl. 2010, 15, 240-247.

Show more citation formats Show less citations formats

Article Metrics

Article Access Statistics



[Return to top]
Math. Comput. Appl. EISSN 2297-8747 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top