Segmentation of Microarray Image Using Information Bottleneck

hari kiran

Segmentation of Microarray Image Using Information Bottleneck

hari kiran * §

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GITAM University

Segmentation of Microarray Image Using Information Bottleneck

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Abstract

DNA microarrays provide a simple tool to identify andquantify the gene expression for tens of thousands of genessimultaneously. The DNA microarray image analysis includes three tasks: gridding, segmentation and intensity extraction.Spots segmentation, which isto distinguish the spot signals from background pixels,is a critical step in microarray image processing. In this paper, new image segmentation algorithm based on the hard version of the information bottleneck method is presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. The input variable here, is the histogram bins and the output variable is the set of regions obtained from the split and merge algorithm. The proposed method is compared with existing segmentation methods such as k-means and Fuzzy C-means. The experimental results show that the proposed algorithm has segmented spots of the microarray image more accurately than other segmentation methods.

References

9 Cites in Article

Reference Format

Mark Schena,Dari Shalon,Ronald Davis,Patrick Brown (1995). Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray.
Wei-Bang Chen,Chengcui Zhang,Wen-Lin Liu An Automated Gridding and Segmentation method for cDNA Microarray Image Analysis.
Tsung-Han Tsai,Chein-Po Yang,Pin-Hua Wei-Chitsai,Chen (2007). Error Reduction on Automatic Segmentation in Microarray Image.
E Erguit,Y Yardimci,E Mumcuoglu,O Konu (2003). Analysis of microarray imagesusing FCM and kmeans Clustering Algorithm.
Volkan Uslan,Omur Ihsan,Bucak (2010). Clustering based Spot Segmentation of cDNA Microarray Images.
C Rafael,Richard Gongalez,Woods Digital Image Processing.
Ting-Quan Deng,Henk Heijmans (2002). Grey-Scale Morphology Based on Fuzzy Logic.
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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

hari kiran. 1970. "Segmentation of Microarray Image Using Information Bottleneck". Global Journal of Computer Science and Technology GJCST Volume 11 (GJCST Volume 11 Issue 19).

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GJCST Volume 11 Issue 19

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

image processing Image Segmentation Microarray

Classification

GJCST Classification I.4.5

I.4.4

Submission ReceivedAugust 27, 2011
Peer Review Double Blind
Handling Editor
Accepted September 24, 2011
Published October 7, 2011

Version of record

v1.2

Issue date

November 11, 2011

Language

English

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Article Matrices

Total Score: 148

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: S.RaghavaRao, M.S.MadhanMohan, Dr.G.M.V.Prasad (PhD/Dr. count: 1)

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Publish Date: 2011 10, Fri

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Total Views: 4.9K

Total Downloads: 413

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