Retinal Image Quality Assessment by Mean-Subtracted Contrast-Normalized Coefficients

  • Adrian GaldranEmail author
  • Teresa Araújo
  • Ana Maria Mendonça
  • Aurélio Campilho
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 27)


The automatic assessment of visual quality on images of the eye fundus is an important task in retinal image analysis. A novel quality assessment technique is proposed in this paper. We propose to compute Mean-Subtracted Contrast-Normalized (MSCN) coefficients on local spatial neighborhoods of a given image and analyze their distribution. It is known that for natural images, such distribution behaves normally, while distortions of different kinds perturb this regularity. The combination of MSCN coefficients with a simple measure of local contrast allows us to design a simple but effective retinal image quality assessment algorithm that successfully discriminates between good and low-quality images, while delivering a meaningful quality score. The proposed technique is validated on a recent database of quality-labeled retinal images, obtaining results aligned with state-of-the-art approaches at a low computational cost.



This work is financed by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme, and by National Funds through the FCT - Fundação para a Ciência e a Tecnologia within project CMUP-ERI/TIC/0028/2014. Teresa Araújo is funded by the FCT grant contract SFRH/BD/122365/2016.


  1. 1.
    Abdel-Hamid, L., El-Rafei, A., El-Ramly, S., Michelson, G., Hornegger, J.: Retinal image quality assessment based on image clarity and content. J. Biomed. Opt. 21(9), 96007 (2016)CrossRefGoogle Scholar
  2. 2.
    Choi, L.K., You, J., Bovik, A.C.: Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans. Image Process. 24(11), 3888–3901 (2015). doi: 10.1109/TIP.2015.2456502 CrossRefMathSciNetGoogle Scholar
  3. 3.
    Fleming, A.D., Philip, S., Goatman, K.A., Olson, J.A., Sharp, P.F.: Automated assessment of diabetic retinal image quality based on clarity and field definition. Invest. Opthalmology Vis. Sci. 47(3), 1120 (2006)CrossRefGoogle Scholar
  4. 4.
    Foracchia, M., Grisan, E., Ruggeri, A.: Luminosity and contrast normalization in retinal images. Med. Image Anal. 9(3), 179–190 (2005)CrossRefGoogle Scholar
  5. 5.
    Geisler, W.S.: Visual perception and the statistical properties of natural scenes. Annu. Rev. Psychol. 59(1), 167–192 (2008)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Hamid, L.A., El-Rafei, A., El-Ramly, S., Michelson, G., Hornegger, J.: No-reference wavelet based retinal image quality assessment. In: Computational Vision and Medical Image Processing V: Proceedings of the 5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing (VipIMAGE), Spain, 2015, p. 123. CRC Press (2015)Google Scholar
  7. 7.
    Kohler, T., Budai, A., Kraus, M.F., Odstrilik, J., Michelson, G., Hornegger, J.: Automatic no-reference quality assessment for retinal fundus images using vessel segmentation. In: Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, pp. 95–100 (2013)Google Scholar
  8. 8.
    Kolar, R., Odstrcilik, J., Jan, J., Harabis, V.: Illumination correction and contrast equalization in colour fundus images. In: European Signal Processing Conference, pp. 298–302 (2011)Google Scholar
  9. 9.
    Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Niemeijer, M., Abrmoff, M.D., van Ginneken, B.: Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening. Med. Image Anal. 10(6), 888–898 (2006)CrossRefGoogle Scholar
  11. 11.
    Pires Dias, J.M., Oliveira, C.M., da Silva Cruz, L.A.: Retinal image quality assessment using generic image quality indicators. Inf. Fusion 19, 73–90 (2014)CrossRefGoogle Scholar
  12. 12.
    Ruderman, D., Bialek, W.: Statistics of natural images: scaling in the woods. Phys. Rev. Lett. 73(6), 814–817 (1994)CrossRefGoogle Scholar
  13. 13.
    Savelli, B., Bria, A., Marrocco, C., Molinara, M., Tortorella, F., Galdran, A., Campilho, A.: Illumination correction by Dehazing for retinal vessel segmentation. In: The 30th IEEE International Symposium on Computer-Based Medical Systems (CBMS) (2017)Google Scholar
  14. 14.
    Sevik, U., Kose, C., Berber, T., Erdol, H.: Identification of suitable fundus images using automated quality assessment methods. J. Biomed. Opt. 19(4), 046006 (2014)CrossRefGoogle Scholar
  15. 15.
    Wang, S., Jin, K., Lu, H., Cheng, C., Ye, J., Qian, D.: Human visual system-based fundus image quality assessment of portable fundus camera photographs. IEEE Trans. Med. Imaging 35(4), 1046–1055 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Adrian Galdran
    • 1
    Email author
  • Teresa Araújo
    • 1
    • 2
  • Ana Maria Mendonça
    • 1
    • 2
  • Aurélio Campilho
    • 1
    • 2
  1. 1.INESC TEC PortoPortoPortugal
  2. 2.Faculdade de EngenhariaUniversidade do PortoPortoPortugal

Personalised recommendations