Skip to main content
Springer Nature Link
Log in

Low-complexity PDE-based approach for automatic microarray image processing

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Microarray image processing is known as a valuable tool for gene expression estimation, a crucial step in understanding biological processes within living organisms. Automation and reliability are open subjects in microarray image processing, where grid alignment and spot segmentation are essential processes that can influence the quality of gene expression information. The paper proposes a novel partial differential equation (PDE)-based approach for fully automatic grid alignment in case of microarray images. Our approach can handle image distortions and performs grid alignment using the vertical and horizontal luminance function profiles. These profiles are evolved using a hyperbolic shock filter PDE and then refined using the autocorrelation function. The results are compared with the ones delivered by state-of-the-art approaches for grid alignment in terms of accuracy and computational complexity. Using the same PDE formalism and curve fitting, automatic spot segmentation is achieved and visual results are presented. Considering microarray images with different spots layouts, reliable results in terms of accuracy and reduced computational complexity are achieved, compared with existing software platforms and state-of-the-art methods for microarray image processing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Agilent Technologies (2012) Feature extraction reference guide. Agilent Technologies Inc, Santa Clara

    Google Scholar 

  2. Alhadidi B, Fakhouri HN, AlMousa OS (2006) cDNA microarray genome image processing using fixed spot position. Am J Appl Sci 3:1730–1734

    Article  Google Scholar 

  3. Angulo J, Serra J (2002) Automatic analysis of DNA microarray images using mathematical morphology. Bioinformatics 19(5):553–562

    Article  Google Scholar 

  4. Angulo J, Serra J (2003) Automatic analysis of DNA microarray images using mathematical morphology. Oxf Bioinform 19(5):553–562

    Article  CAS  Google Scholar 

  5. Bajcsy P (2004) Gridline, automatic grid alignment in DNA microarray scans. IEEE Trans Image Process 13:15–25

    Article  PubMed  Google Scholar 

  6. Bajcsy P (2006) An overview of DNA microarray grid alignment and foreground separation approaches. EURASIP J Appl Sig Process 2:1–13

    Article  Google Scholar 

  7. Bariamis D, Iakovidis DK, Maroulis D (2010) M3G: maximum margin microarray gridding. BMC Bioinformatics 11:49

    Article  PubMed Central  PubMed  Google Scholar 

  8. Bariamis D, Maroulis D, Iakovidis DK (2010) Unsupervised SVM-based gridding for DNA microarray images. Comput Med Imaging Graph 34:418–425

    Article  PubMed  Google Scholar 

  9. Belean B, Borda M, Le Gal B, Terebes R (2012) FPGA based system for automatic cDNA microarray image processing. Comput Med Imaging Graph 36(5):419–429

    Article  PubMed  Google Scholar 

  10. Blekas K, Galatsanos NP, Likas A, Lagaris IE (2005) A Mixture model analysis of DNA microarray images. IEEE Trans Med Imaging 24(7):901–909

    Article  CAS  PubMed  Google Scholar 

  11. Bozinov D, Rahnenfuhrer J (2002) Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering. Bioinformatics 18(5):747–756

    Article  CAS  PubMed  Google Scholar 

  12. Brandle N, Bischof H, Lapp H (2003) Robust DNA microarray image analysis. Mach Vis Appl 15:11–28

    Article  Google Scholar 

  13. Campbell AM, Hatfield WT, Heyer LJ (2007) Make microarray data with known ratios. CBE Life Sci Educ 6:196–197

    Article  PubMed Central  PubMed  Google Scholar 

  14. Demirkaya O, Asyali MH, Shoukri MM (2005) Segmentation of cDNA microarray spots using markov random field modelling. Bioinformatics 21(13):2994–3000

    Article  CAS  PubMed  Google Scholar 

  15. Emerich S, Lupu E, Arsinte R (2011) A new approach to iris recognition. In: Proceedings of the IEEE international symposium on signals, circuits and systems, ISSCS, pp 1–4

  16. Florea L, Florea C, Vertan C, Sultana A (2011) Automatic tools for diagnosis support of total hip replacement follow-up. Adv Electr Comput Eng 11(4):55–62

    Article  Google Scholar 

  17. Giannakeas N, Fotiadis D (2009) An automated method for gridding and clustering-based segmentation of cDNA microarray images. Comput Med Imaging Graph 33(1):40–49

    Article  PubMed  Google Scholar 

  18. Giannakeas N, Kalatzis F, Tsipouras M, Fotiadis D (2012) Spot addressing for microarray images structured in hexagonal grids. Comput Methods Programs Biomed 106(1):1–13

    Article  PubMed  Google Scholar 

  19. Gómez P, Díaz F, Martínez R, Malutan R, Rodellar V, Puntonet CG (2006) Robust preprocessing of gene expression microarrays for independent component analysis. Lect Notes Comput Sci ICA 3889:714–721

    Article  Google Scholar 

  20. Handran S, Zhai YZ (2003) Biological relevance of GenePix results. Molecular Devices - Application Notes, pp 1–9

  21. Ho J, Hwang W (2008) Automatic microarray spot segmentation using a snake-fisher model. IEEE Trans Med Imaging 27(6):847–857

    Article  PubMed  Google Scholar 

  22. Hochbaum D (2001) An efficient algorithm for image segmentation, markov random fields and related problems. J ACM 48(4):686–701

    Article  Google Scholar 

  23. Katzer M, Kummert F, Sagerer G (2003) Methods for automatic microarray image segmentation. IEEE Trans Nanobiosci 2(4):202–214

    Article  Google Scholar 

  24. Kim KY, Kim J, Kim HJ, Nam W, Cha IH (2010) A method for detecting significant genomic regions associated with oral squamous cell carcinoma using aCGH. Med Biol Eng Compu 48(5):459–468

    Article  Google Scholar 

  25. Kornaros G, Blionas S (2008) Microarchitecture of a multicore SoC for data analysis of a lab-on-chip microarray. EURASIP J Adv Signal Process 520641:1–11

    Google Scholar 

  26. Li Q, Fraley C, Bumgarner R, Yeung K, Raftery A (2005) Donuts, scratches and blanks: robust model-based segmentation of microarray images. Bioinformatics 21(12):2875–2882

    Article  CAS  PubMed  Google Scholar 

  27. Ludusan C, Lavialle O (2012) Multifocus image fusion and denoising: a variational approach. Pattern Recogn Lett 33(10):1388–1396

    Article  Google Scholar 

  28. Malutan R, Gómez P, Borda M (2010) Independent component analysis algorithms for microarray data analysis. Intell Data Anal 14(2):193–206

    Google Scholar 

  29. Osher S, Rudin L (1990) Feature-oriented image enhancement using shock filters. SIAM J 27:919–940

    Google Scholar 

  30. Rahnenfuhrer J, Bozinov D (2004) Hybrid clustering for microarray image analysis combining intensity and shape features. BMC Bioinformatics 5:47

    Article  PubMed Central  PubMed  Google Scholar 

  31. Roy K, Bhattacharya P (2009) Iris recognition in non-ideal situations. Lect Notes Comput Sci Inf Secur 5735:143–150

    Article  Google Scholar 

  32. Rueda L, Rezaeian I (2011) A fully automatic gridding method for cDNA microarray images. BMC Bioinformatics 12(113):1–17

    Google Scholar 

  33. Rueda L, Vidyadharan V (2006) A Hill-climbing approach for automatic gridding of cDNA microarray images. IEEE/ACM Trans Comput Biol Bioinf 3(1):72–83

    Article  CAS  Google Scholar 

  34. Schena M (2003) Microarray analysis. Wiley, New York

    Google Scholar 

  35. Steinfath M, Wruck W (2001) Automated image analysis for array hybridization experiments. Oxf J Bioinform 17(7):634–641

    Article  CAS  Google Scholar 

  36. Verdnik D (2004) Guide to microarray analyses. GenePix Pro, MDS Analytical Technologies, Sunnyvale, CA

  37. Wang Y, Marc QM, Zhang K, Shih YF (2007) A hierarchical refinement algorithm for fully automatic gridding in spotted DNA microarray image processing. Inf Sci Int J 177(4):1123–1135

    Google Scholar 

  38. Zacharia E, Maroulis D (2008) An original genetic approach to the fully automatic gridding of microarray images. IEEE Trans Med Imaging 27(6):805–813

    Article  PubMed  Google Scholar 

  39. Zacharia E, Maroulis D (2010) 3D spot-modeling for automatic segmentation of microarray images. IEEE Trans Nanobiosci 9(3):181–192

    Article  Google Scholar 

  40. Zhang M, Mao K, Tao W, Tarn T (2006) A computational method to geometric measure of biological particles and application to DNA microarray spot size estimation. Med Biol Eng Compu 44:275–279

    Article  Google Scholar 

  41. Zhang K, Song H, Zhang L (2010) Active contours driven by local image fitting energy. Pattern Recogn 43:1199–1206

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the European Social Fund through the POSDRU Program, DMI 1.5, ID 137516-PARTING.

Author information

Authors and Affiliations

  1. Department of Mass Spectrometry, Chromatography and Applied Physics, National Institute for Research and Development of Isotopic and Molecular Technologies, Cluj-Napoca, Romania

    Bogdan Belean & Adrian Bot

  2. Department of Communications, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

    Bogdan Belean & Romulus Terebes

Authors
  1. Bogdan Belean
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Romulus Terebes
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Adrian Bot
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Bogdan Belean.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Belean, B., Terebes, R. & Bot, A. Low-complexity PDE-based approach for automatic microarray image processing. Med Biol Eng Comput 53, 99–110 (2015). https://doi.org/10.1007/s11517-014-1214-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-014-1214-2

Keywords