Skip to main content

Extended-Regular Sequence for Automated Analysis of Microarray Images


Microarray study enables us to obtain hundreds of thousands of expressions of genes or genotypes at once, and it is an indispensable technology for genome research. The first step is the analysis of scanned microarray images. This is the most important procedure for obtaining biologically reliable data. Currently most microarray image processing systems require burdensome manual block/spot indexing work. Since the amount of experimental data is increasing very quickly, automated microarray image analysis software becomes important. In this paper, we propose two automated methods for analyzing microarray images. First, we propose the extended-regular sequence to index blocks and spots, which enables a novel automatic gridding procedure. Second, we provide a methodology, hierarchical metagrid alignment, to allow reliable and efficient batch processing for a set of microarray images. Experimental results show that the proposed methods are more reliable and convenient than the commercial tools.


  1. Duggan DJ, Bittner ML, Chen Y, Meltzer P, Trent JM: Expression profiling using cDNA microarrays. Nature genetics 1999, 21: 10–14.

    Article  Google Scholar 

  2. Shalon D, Smith SJ, Brown PO: A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. Genome Research 1996, 6(7):639–645. 10.1101/gr.6.7.639

    Article  Google Scholar 

  3. Alizadeh AA, Eisen MB, Davis RE, et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000, 403(6769):503–511. 10.1038/35000501

    Article  Google Scholar 

  4. Steinfath M, Wruck W, Seidel H, Lehrach H, Radelof U, O'Brien J: Automated image analysis for array hybridization experiments. Bioinformatics 2001, 17(7):634–641. 10.1093/bioinformatics/17.7.634

    Article  Google Scholar 

  5. Hirata R Jr., Barrera J, Hashimoto RF, Dantas DO: Microarray gridding by mathematical morphology. Proceedings of 14th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI '01), October 2001, Florianópolis, Brazil 112–119.

    Chapter  Google Scholar 

  6. Jung H-Y, Cho H-G:An automatic block and spot indexing with-nearest neighbors graph for microarray image analysis. Bioinformatics 2002, 18(suppl 2):S141–S151. 10.1093/bioinformatics/18.suppl_2.S141

    Article  Google Scholar 

  7. Kauer G, Blöcker H: Analysis of disturbed images. Bioinformatics 2004, 20(9):1381–1387. 10.1093/bioinformatics/bth099

    Article  Google Scholar 

  8. Srinark T, Kambhamettu C: A microarray image analysis system based on multiple-snake. Journal of Biological Systems Special Issue 2004, 12(4):202–209.

    Google Scholar 

  9. Robins G, Robinson BL, Sethi BS: On detecting spatial regularity in noisy images. Information Processing Letters 1999, 69(4):189–195. 10.1016/S0020-0190(99)00013-7

    Article  MathSciNet  Google Scholar 

  10. Kahng AB, Robins G: Optimal algorithms for extracting spatial regularity in images. Pattern Recognition Letters 1991, 12(12):757–764. 10.1016/0167-8655(91)90073-U

    Article  Google Scholar 

  11. GenePix

  12. ImaGene

  13. Caetano TS, Caelli T, Barone DAC: An optimal probabilistic graphical model for point set matching. Proceedings of Joint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition (S+SSPR '04), August 2004, Lisbon, Portugal 162–170.

    Chapter  Google Scholar 

  14. Spencer J: Ten Lectures on the Probabilistic Method. SIAM, Philadelphia, Pa, USA; 1990.

    MATH  Google Scholar 

  15. Bollobás B: Random Graphs. Cambridge University Press, Cambridge, UK; 2001.

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Hee-Jeong Jin.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Jin, HJ., Chun, BK. & Cho, HG. Extended-Regular Sequence for Automated Analysis of Microarray Images. EURASIP J. Adv. Signal Process. 2006, 013623 (2006).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: