Signal, Image and Video Processing

, Volume 12, Issue 6, pp 1069–1077 | Cite as

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

  • Tiejun Li
  • Guifang ShaoEmail author
  • Yue Sun
  • Weiren Shi
Original Article


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.


cDNA microarray Contrast enhancement Spot Gridding Segmentation 



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).


  1. 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)Google Scholar
  2. 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)CrossRefGoogle Scholar
  3. 3.
    Nagaraja, J., Manjunath, S.S.: A fully automatic approach for enhancement of microarray images. J. Autom. Control Eng. 1(4), 285–289 (2013)CrossRefGoogle Scholar
  4. 4.
    Rueda, L., Rezaeian, I.: A fully automatic gridding method for cDNA microarray images. BMC Bioinform. 12(113), 1–17 (2011)Google Scholar
  5. 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)CrossRefGoogle Scholar
  6. 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)CrossRefGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 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. 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. 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)Google Scholar
  11. 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. 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)Google Scholar
  13. 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)CrossRefGoogle Scholar
  14. 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)CrossRefGoogle Scholar
  15. 15.
    Zifan, A., Moradi, M.H., Gharibzadeh, S.: Microarray image enhancement by denoising using decimated and undecimated multiwavelet transforms. SIViP 4, 177–185 (2010)CrossRefzbMATHGoogle Scholar
  16. 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. 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)CrossRefGoogle Scholar
  18. 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)zbMATHGoogle Scholar
  19. 19.
    Kaur, A., Singh, C.: Contrast enhancement for cephalometric images using wavelet-based modified adaptive histogram equalization. Appl. Soft Comput. 51, 180–191 (2017)CrossRefGoogle Scholar
  20. 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)CrossRefGoogle Scholar
  21. 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)CrossRefGoogle Scholar
  22. 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. 23.
    Kaur, A., Singh, C.: Contrast enhancement for cephalometric images using wavelet-based modified adaptive histogram equalization. Appl. Soft Comput. 51, 180–191 (2017)CrossRefGoogle Scholar
  24. 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)CrossRefGoogle Scholar

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© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institution of AutomationChongqing UniversityChongqingChina
  2. 2.School of Information EngineeringJimei UniversityXiamenChina
  3. 3.Department of AutomationXiamen UniversityXiamenChina

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