An Overview of DNA Microarray Grid Alignment and Foreground Separation Approaches

  • Peter BajcsyEmail author
Open Access
Research Article
Part of the following topical collections:
  1. Advanced Signal Processing Techniques for Bioinformatics


This paper overviews DNA microarray grid alignment and foreground separation approaches. Microarray grid alignment and foreground separation are the basic processing steps of DNA microarray images that affect the quality of gene expression information, and hence impact our confidence in any data-derived biological conclusions. Thus, understanding microarray data processing steps becomes critical for performing optimal microarray data analysis. In the past, the grid alignment and foreground separation steps have not been covered extensively in the survey literature. We present several classifications of existing algorithms, and describe the fundamental principles of these algorithms. Challenges related to automation and reliability of processed image data are outlined at the end of this overview paper.


Information Technology Image Data Fundamental Principle Microarray Data Quantum Information 


  1. 1.
    Schena M, Shalon D, Davis RW, Brown PO: Quantitative monitoring of gene expression patterns with complementary DNA microarray. Science 1995, 270: 467–470. 10.1126/science.270.5235.467CrossRefGoogle Scholar
  2. 2.
    Fenstemacher D: Introduction to bioinformatics. Journal of the American Society for Information Science and Technology 2005, 65(5):440–446.CrossRefGoogle Scholar
  3. 3.
    MacMullen WJ, Denn SO: Information problems in molecular biology and bioinformatics. Journal of the American Society for Information Science and Technology 2005, 65(5):447–456.CrossRefGoogle Scholar
  4. 4.
    Quackenbush J: Computational analysis of microarray. Computational Analysis of Microarray 2001, 2(6):418–427.Google Scholar
  5. 5.
    Bajcsy P, Han J, Liu L, Young J: Survey of bioData analysis from data mining perspective. In Data Mining in Bioinformatics. Edited by: Wang JTL, Zaki MJ, Toivonen HTT, Shasha D. Springer, New York, NY, USA; 2004:9–39. chapter 2Google Scholar
  6. 6.
    Baldi P, Brunak S: Bioinformatics, The Machine Learning Approach. 2nd edition. The MIT Press, Cambridge, Mass, USA; 2001.zbMATHGoogle Scholar
  7. 7.
    Golub TR, Slonim DK, Tamayo P, et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286(5439):531–537. 10.1126/science.286.5439.531CrossRefGoogle Scholar
  8. 8.
    Moore SK: Understanding the human genome. IEEE Spectrum 2000, 37(11):33–42. 10.1109/6.880951CrossRefGoogle Scholar
  9. 9.
    Goryachev AB, MacGregor PF, Edwards AM: Unfolding of microarray data. Journal of Computational Biology 2001, 8(4):443–461. 10.1089/106652701752236232CrossRefGoogle Scholar
  10. 10.
    Bajcsy P: An overview of microarray image processing requirements. The IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), the Workshop on Computer Vision Methods for Bioinformatics (CVMB), June 2005, San Diego, Calif, USAGoogle Scholar
  11. 11.
    Brazma A, Hungamp P, Quackenbush J, et al.: Minimum information about a microarray experiment (MIAME) - toward standards for microarray data. Nature Genetics 2001, 29(4):365–371. 10.1038/ng1201-365CrossRefGoogle Scholar
  12. 12.
    Kamberova G, Shah S (Eds): DNA Array Image Analysis - Nuts and Bolts. Data Analysis Tools for DNA Microarrays. DNA Press LLC, Salem, Mass, USA; 2002.Google Scholar
  13. 13.
    Srinark T, Kambhamettu C: A microarray image analysis system based on multiple-snake. Journal of Biological Systems 2004., 12(4): Special issuezbMATHGoogle Scholar
  14. 14.
    Yue H, Eastman PS, Wang BB, et al.: An evaluation of the performance of cDNA microarrays for detecting changes in global mRNA expression. Nucleic Acids Research 2001, 29(8):e41–1.CrossRefGoogle Scholar
  15. 15.
    Draghici S: Data Analysis Tools for DNA Microarrays, CRC Mathematical Biology and Medicine Series. Chapman & Hall, London, UK; 2003.CrossRefGoogle Scholar
  16. 16.
    Han J, Kamber M: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco, Calif, USA; 2001.zbMATHGoogle Scholar
  17. 17.
    Saal LH, Troein C, Vallon-Christersson J, Gruvberger S, Borg Å, Peterson C: BioArray software environment: a platform for comprehensive management and analysis of microarray data. Genome Biology 2002., 3(8): software 0003.1-0003.6Google Scholar
  18. 18.
    Samartzidou H, Turner L, Houts T: Lucidea Microarray ScoreCard: An integrated tool for validation of microarray gene expression experiments," Innovation Forum, Microarrays. Life Science News 8, 2001 Amersham Pharmacia BiotechGoogle Scholar
  19. 19.
    Rocke D, Durbin B: A model for measurement error for gene expression arrays. Journal of Computational Biology 2001, 8(6):557–569. 10.1089/106652701753307485CrossRefGoogle Scholar
  20. 20.
    Seo J, Shneiderman B: Interactively exploring hierarchical clustering results. IEEE Computer 2002, 35(7):80–86. 10.1109/MC.2002.1016905CrossRefGoogle Scholar
  21. 21.
    Balagurunathan Y, Dougherty ER, Chen Y, Bittner ML, Trent JM: Simulation of cDNA microarrays via a parameterized random signal model. Journal of Biomedical Optics 2002., 7(3):CrossRefGoogle Scholar
  22. 22.
    Brandle N, Bischof H, Lapp H: Robust DNA Microarray image analysis. Machine Vision and Applications 2003, 15(1):11–28. 10.1007/s00138-002-0114-xCrossRefGoogle Scholar
  23. 23.
    Whitfield CW, Cziko AM, Robinson GE: Gene expression profiles in the brain predict behavior in individual honey bees. Science 2003, 302: 296–299. 10.1126/science.1086807CrossRefGoogle Scholar
  24. 24.
    Bajcsy P: Gridline: automatic grid alignment in DNA microarray scans. IEEE Transactions on Image Processing 2004, 13(1):15–25. 10.1109/TIP.2003.819941MathSciNetCrossRefGoogle Scholar
  25. 25.
    Jung H-Y, Cho H-G: An automatic block and spot indexing with k-nearest neighbors graph for microarray image analysis. Bioinformatics 2002, 18(2):S141–S151. 10.1093/bioinformatics/18.suppl_2.S141CrossRefGoogle Scholar
  26. 26.
    Axon Instruments Inc : GenePix Pro, Product Description.
  27. 27.
    Eisen M: ScanAlyze. Product Description at
  28. 28.
    Scanalytics Inc : MicroArray Suite. Product Description at
  29. 29.
    Buhler J, Ideker T, Haynor D: Dapple: improved techniques for finding spots on DNA microarrays. In Tech. Rep. UWTR 2000-08-05. UV CSE, Seattle, Wash, USA;Google Scholar
  30. 30.
    Biodiscovery Inc : ImaGene Product description. 2005. Scholar
  31. 31.
    Packard BioChip Technologies, LLC, "Quant Array Analysis Software," Product Description at
  32. 32.
    Imaging Research Inc : Array Vision. Product Description at
  33. 33.
    Hartelius K, Cartstensen JM: Bayesian grid matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003, 25(2):162–173. 10.1109/TPAMI.2003.1177149CrossRefGoogle Scholar
  34. 34.
    Bajcsy P: Image To Knowledge (I2K). Software Documentation at
  35. 35.
    CSIRO Mathematical and Informational Sciences : SpotImage Analysis Software. Product Documentation at
  36. 36.
    Jain AN, Tokuyasu TA, Snijders AM, Segraves R, Albertson DG, Pinkel D: Fully automated quantification of microarray image data. Genome Research 2002, 12(2):325–332. 10.1101/gr.210902CrossRefGoogle Scholar
  37. 37.
    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/17.7.634CrossRefGoogle Scholar
  38. 38.
    Katzer M, Kummert F, Sagerer G: Robust automatic microarray image analysis. Proceedings of the International Conference on Bioinformatics: North-South Networking, 2002, Bangkok, ThailandGoogle Scholar
  39. 39.
    Katzer M, Kummert F, Sagerer G: Methods for automatic microarray image segmentation. IEEE Transactions on Nanobioscience 2003, 2(4):202–212. 10.1109/TNB.2003.817023CrossRefGoogle Scholar
  40. 40.
    Liew AW-C, Yan H, Yang M: Robust adaptive spot segmentation of DNA microarray images. Pattern Recognition 2003, 36(5):1251–1254. 10.1016/S0031-3203(02)00170-XCrossRefGoogle Scholar
  41. 41.
    Russ J: The Image Processing Handbook. 3rd edition. CRC Press LLC, Boca Raton, Fla, USA; 1999.zbMATHGoogle Scholar
  42. 42.
    Angulo J, Serra J: Automatic analysis of DNA microarray images using mathematical morphology. Bioinformatics 2003, 19(5):553–562. 10.1093/bioinformatics/btg057CrossRefGoogle Scholar
  43. 43.
    Hirata R, 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, Florianopolis, Brazil 112–119.CrossRefGoogle Scholar
  44. 44.
    Antoniol G, Ceccarelli M: A markov random field approach to microarray image gridding. Proceedings of the 17th International Conference on Pattern Recognition (ICPR '04) , August 2004, Cambridge, UKGoogle Scholar
  45. 45.
    Demirkaya O, Asyali MH, Shoukri MM: Segmentation of cDNA microarray spots using Markov random field modeling. Bioinformatics 2005, 21(13):2994–3000. 10.1093/bioinformatics/bti455CrossRefGoogle Scholar
  46. 46.
    Jin H-J, Chun B-K, Cho HG: Extended epsilon regular sequence for automated analysis of microarray images. The IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), the Workshop on Computer Vision Methods for Bioinformatics (CVMB), June 2005, San Diego, Calif, USAGoogle Scholar
  47. 47.
    Bozinov D, Rahnenfuhrer J: Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering. Bioinformatics 2002, 18(5):747–756. 10.1093/bioinformatics/18.5.747CrossRefGoogle Scholar
  48. 48.
    Tou JT, Gonzales RC: Pattern Recognition Principles. Addison-Wesley, Reading, Mass, USA; 1974.Google Scholar
  49. 49.
    Rahnenführer J, Bozinov D: Hybrid clustering for microarray image analysis combining intensity and shape features. BMC Bioinformatics 2004, 5(1):47. 10.1186/1471-2105-5-47CrossRefGoogle Scholar
  50. 50.
    Chen Y, Dougherty ER, Bittner ML: Ratio-based decisions and the quantitative analysis of cDNA microarray images. Journal Of Biomedical Optics 1997, 2(4):364–374. 10.1117/12.281504CrossRefGoogle Scholar
  51. 51.
    Sheskin DJ: Handbook of Parametric and Nonparametric Statistical Procedures. 2nd edition. Chapman & Hall CRC, London, UK; 2000.zbMATHGoogle Scholar
  52. 52.
    Lukac R, Plataniotis KN, Smolka B, Venetsanopoulos AN: An automated multichannel procedure for cDNA microarray image processing. Lecture Notes in Computer Science 2004, 3212: 1–8. 10.1007/978-3-540-30126-4_1CrossRefGoogle Scholar
  53. 53.
    Adams RM, Stancampiano B, McKenna M, Small D: Case study: a virtual environment for genomic data visualization. IEEE Transactions on Visualization 2002., 1: October 27–November 1, 2002, Boston, Mass, USA (published as CD)Google Scholar
  54. 54.
    Lawrence ND, Milo M, Niranjan M, Rashbass P, Soullier S: Reducing the variability in cDNA microarray image processing by Bayesian inference. Bioinformatics 2004, 20(4):518–526. 10.1093/bioinformatics/btg438CrossRefGoogle Scholar
  55. 55.
    Foster I, Kesselman C: Computational grids. In The Grid: Blueprint for a New Computing Infrastructure. Morgan-Kaufman, San Francisco, Calif, USA; 1999. chapter 2Google Scholar
  56. 56.
    Karo M, Dwan C, Freeman J, Weissman J, Livny M, Retzel E: Applying grid technologies to bioinformatics. Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing (HPDC '01), August 2001, San Francisco, Calif, USA 441–442.CrossRefGoogle Scholar
  57. 57.
    Strom CM, Clark DD, Hantash FM, et al.: Direct visualization of cystic fibrosis transmembrane regulator mutations in the clinical laboratory setting. Clinical Chemistry 2004, 50(5):836–845. 10.1373/clinchem.2003.026088CrossRefGoogle Scholar
  58. 58.
    Report of the National Science Foundation Blue-Ribbon Advisory Panel on Cyberinfrastructure,

Copyright information

© Bajcsy 2006

Authors and Affiliations

  1. 1.The National Center for Supercomputing ApplicationsUniversity of Illinois at Urbana-ChampaignUSA

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