Skip to main content

Advertisement

Springer Nature Link
Log in

Contrast enhancement for cDNA microarray image based on fourth-order moment

  • Original Article
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Microarray technology, which can monitor the expression levels of thousands of genes simultaneously, has been widely used in biological experiment. Image processing, as one key step in microarray technology, plays an essential role in microarray analysis. Meanwhile, biological applications require a higher accuracy in each image processing step. However, the low contrast levels of image make it difficult to obtain better processing precision. This paper proposes a fully automatic contrast enhancement (CE) method based on fourth-order moment. Also, a background estimation method is presented to obtain a better microarray image processing result. Comparative results on contrast enhance and gridding revealed that the proposed CE algorithm performs better compared to the adaptive histogram equalization method. Numerous experiments on the Swiss Institute of Bioinformatics (SIB), Joe DeRisi individual (DeRisi), Gene Expression Omnibus (GEO), and Stanford Microarray Database (SMD) data sets also indicate that the proposed CE exerts a tremendous effect on gridding, but has nothing to do with segmentation.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Hernández-Cabronero, M., Sanchez, V., Marcellin, M.W., Serra-Sagristà, J.: A distortion metric for the lossy compression of DNA microarray images. In: Data Compression Conference, Snowbird, UT, United States, pp. 171–180 (2013)

  2. Katsigiannis, S., Zacharia, E., Maroulis, D.: MIGS–GPU: microarray image gridding and segmentation on the GPU. IEEE J. Biomed. Health Inform. 21(3), 867–874 (2017)

    Article  Google Scholar 

  3. Nagaraja, J., Manjunath, S.S.: A fully automatic approach for enhancement of microarray images. J. Autom. Control Eng. 1(4), 285–289 (2013)

    Article  Google Scholar 

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

    Google Scholar 

  5. Giannakeas, N., Kalatzis, F., Tsipouras, M.G., Fotiadis, D.I.: A generalized methodology for the gridding of microarray images with rectangular or hexagonal grid. SIViP 10(4), 719–728 (2016)

    Article  Google Scholar 

  6. Shao, G.F., Yang, F., Zhang, Q., Zhou, Q.F., Luo, L.K.: Using the maximum between-class variance for automatic gridding of cDNA microarray images. IEEE/ACM Trans. Comput. Biol. Bioinf. 10(1), 181–192 (2013)

    Article  Google Scholar 

  7. Labib, F.E.Z., Fouad, I., Mabrouk, M., Sharawy, A.: An efficient fully automated method for gridding microarray images. Am. J. Biomed. Eng. 2(3), 115–119 (2012)

    Article  Google Scholar 

  8. Harikiran, J., Avinash, B., Lakshmi, P., Kirankumar, R.: Automatic gridding method for microarray images. J. Theor. Appl. Inf. Technol. 65(1), 235–241 (2014)

    Google Scholar 

  9. Maguluri, L.P., Rajapanthula, K., Parvathaneni, N.S.: A comparative analysis of clustering based segmentation algorithms in microarray images. Int. J. Emerg. Sci. Eng. 1(5), 27–32 (2013)

    Google Scholar 

  10. Mouysset, S., Guivarch, R., Noailles, J., Ruiz, D.: Parallel spectral clustering for the segmentation of cDNA microarray images. In: The 6th International Conference on PACBB, Salamanca, Spain, vol. 154, pp. 1–9 (2012)

  11. Harikiran, J., RamaKrishna, D., Phanendra, M.L., Lakshmi, P.V., Kiran, R.: Fuzzy c-means with Bi-dimensional empirical mode decomposition for segmentation of microarray image. Int. J. Comput. Sci. Issues 9(3), 316–321 (2012)

    Google Scholar 

  12. Meher, J.K., Meher, P.K., Dash, G.N.: Preprocessing of microarray by integrated OSR and SDF approach for effective denoising and quantification. In: International Conference on Information and Network Technology, Singapore, pp. 158–163 (2011)

  13. Fouad, I.A., Mabrouk, M.S., Sharawy, A.A.: A fully automated method for noisy cDNA microarray image quantification. Int. J. Comput. Technol. 11(3), 2330–2340 (2013)

    Article  Google Scholar 

  14. Srinivasan, L., Rakvongthai, Y., Oraintara, S.: Microarray image denoising using complex gaussian scale mixtures of complex wavelets. IEEE J. Biomed. Health Inform. 18(4), 1423–1430 (2014)

    Article  Google Scholar 

  15. Zifan, A., Moradi, M.H., Gharibzadeh, S.: Microarray image enhancement by denoising using decimated and undecimated multiwavelet transforms. SIViP 4, 177–185 (2010)

    Article  MATH  Google Scholar 

  16. Kakumani, A., Mendhurwar, K.A., Kakumani, R.: Microarray image denoising using independent component analysis. Int. J. Comput. Appl. 1(11), 87–95 (2010)

    Google Scholar 

  17. Saberkari, H., Shamsi, M., Ghavifekr, H.: A shape-independent algorithm for fully-automated gridding of cDNA microarray images. Comput. Electr. Eng. 62, 135–150 (2017)

    Article  Google Scholar 

  18. Kaur, M., Kaur, J., Kaur, J.: Survey of contrast enhancement techniques based on histogram equalization. Int. J. Adv. Comput. Sci. Appl. 2(7), 137–141 (2011)

    MATH  Google Scholar 

  19. Kaur, A., Singh, C.: Contrast enhancement for cephalometric images using wavelet-based modified adaptive histogram equalization. Appl. Soft Comput. 51, 180–191 (2017)

    Article  Google Scholar 

  20. Das, D., Mukhopadhyay, S., Praveen, S.R.S.: Multi-scale contrast enhancement of oriented features in 2D images using directional morphology. Opt. Laser Technol. 87, 51–63 (2017)

    Article  Google Scholar 

  21. Shakeri, M., Dezfoulian, M.H., Khotanlou, H.: Image contrast enhancement using fuzzy clustering with adaptive cluster parameter and sub-histogram equalization. Digit. Signal Proc. 62, 224–237 (2017)

    Article  Google Scholar 

  22. Nimkar, S., Varghese, S., Shrivastava, S.: Contrast enhancement and brightness preservation using multi-decomposition histogram equalization. Int. J. Signal Image Process. 4(3), 85–93 (2013)

    Google Scholar 

  23. Kaur, A., Singh, C.: Contrast enhancement for cephalometric images using wavelet-based modified adaptive histogram equalization. Appl. Soft Comput. 51, 180–191 (2017)

    Article  Google Scholar 

  24. Wang, T.N., Li, T.J., Shao, G.F., Wu, S.X.: An improved K-means clustering method for cDNA microarray image segmentation. Genet. Mol. Res. 14(3), 7771–7781 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 61403318) and the Fundamental Research Funds for the Central Universities of China (Grant No. 20720160085).

Author information

Authors and Affiliations

  1. Institution of Automation, Chongqing University, Chongqing, 400044, China

    Tiejun Li, Yue Sun & Weiren Shi

  2. School of Information Engineering, Jimei University, Xiamen, 361021, China

    Tiejun Li

  3. Department of Automation, Xiamen University, Xiamen, 361005, China

    Guifang Shao

Authors
  1. Tiejun Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Guifang Shao
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yue Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Weiren Shi
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Guifang Shao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, T., Shao, G., Sun, Y. et al. Contrast enhancement for cDNA microarray image based on fourth-order moment. SIViP 12, 1069–1077 (2018). https://doi.org/10.1007/s11760-018-1258-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-018-1258-0

Keywords