Skip to main content
SpringerLink
Log in

An improved luminosity and contrast enhancement framework for feature preservation in color fundus images

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

Abstract

Insufficient luminosity and poor local contrast are the major hurdles affecting the visual quality of the fundus images. A suitable framework is proposed for the enhanced visual perception of color fundus images based on a hybrid approach that combines gamma correction and singular value equalization for luminosity enhancement and contrast-limited adaptive histogram equalization (CLAHE) for local contrast enhancement. Luminosity enhancement is done by performing singular value equalization of the low-frequency component of the original value channel of the image in hue, saturation, and value color space using the low-frequency component of the gamma-corrected value channel of the same image. Discrete wavelet transform is applied for extracting the corresponding low-frequency components from the original and gamma-corrected value channels. Local contrast enhancement is achieved using CLAHE performed on the luminosity channel in \(L^*a^*b^*\) color space. The performance of the proposed method is analyzed qualitatively based on visual assessment and quantitatively with the parameters such as peak signal-to-noise ratio, absolute mean brightness error, discrete entropy and measure of enhancement. Experiments conducted on the color fundus images show improved results with sufficient detail preservation and enhanced visual perception compared to the existing methods.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. 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)

    Article  Google Scholar 

  2. Hassan, G., Hassanien, A.E.: Retinal fundus vasculature multilevel-segmentation using whale optimization algorithm. Signal Image Video Process. 12(2), 263–70 (2018)

    Article  Google Scholar 

  3. Davis, H., Russell, S., Barriga, E., Abramoff, M., Soliz, P.: Vision-based real-time retinal image quality assessment. In: IEEE International Symposium on Computer-Based Medical Systems, pp. 1–6 (2009)

  4. Soomro, T.A., Gao, J., Khan, M.A.U., Khan, T.M., Paul, M.: Role of image contrast enhancement technique for ophthalmologist as diagnostic tool for diabetic retinopathy. In: International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE (2016)

  5. Hani, A.F.M., Soomro, T.A., Faye, I., Kamel, N., Yahya, N.: Denoising methods for retinal fundus images. In: 5th International Conference on Intelligent and Advanced Systems (ICIAS), pp. 1–6. IEEE (2014)

  6. Yao, M., Zhu, C.: Study and comparison on histogram-based local image enhancement methods. In: 2017 2nd International Conference on Image, Vision and Computing (ICIVC), pp. 309–314. IEEE (2017)

  7. Wang, X., Chen, L.: Contrast enhancement using feature-preserving bi-histogram equalization. Signal Image Video Process. 12(4), 685–92 (2018)

    Article  Google Scholar 

  8. Zhao, Z., Zhou, Y.: An image contrast enhancement algorithm using PLIP-based histogram modification. In: 2017 3rd IEEE International Conference on Cybernetics (CYBCONF), pp. 1–4. IEEE (2017)

  9. Kim, J.Y., Kim, L.S., Hwang, S.H.: An advanced contrast enhancement using partially overlapped sub-block histogram equalization. IEEE Trans. Circuits Syst. Video Technol. 11(4), 475–484 (2001)

    Article  Google Scholar 

  10. Lidong, H., Wei, Z., Jun, W., Zebin, S.: Combination of contrast limited adaptive histogram equalisation and discrete wavelet transform for image enhancement. IET Image Process. 9(10), 908–915 (2015)

    Article  Google Scholar 

  11. Hani, A.F.M., Soomro, T.A., Fayee, I.: Non-invasive contrast enhancement for retinal fundus imaging. In: IEEE International Conference on Control System, Computing and Engineering, vol. 2013, pp. 197–202 (2013)

  12. Demirel, H., Anbarjafari, G., Jahromi, M.N.S.: Image equalization based on singular value decomposition. In: 2008 23rd International Symposium on Computer and Information Sciences, pp. 1–5. IEEE (2008)

  13. Ozcinar, C., Demirel, H., Anbarjafari, G.: Image equalization using singular value decomposition and discrete wavelet transform. In: Discrete Wavelet Transforms—Theory and Applications. Intech (2011)

  14. Sharma, N., Verma, O.P.: Gamma correction based satellite image enhancement using singular value decomposition and discrete wavelet transform. In: 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, pp. 1286–1289. IEEE (2014)

  15. Huang, S.C., Cheng, F.C., Chiu, Y.S.: Efficient contrast enhancement using adaptive gamma correction with weighting distribution. IEEE Trans. Image Process. 22(3), 1032–1041 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  16. Kallel, F., Sahnoun, M., Hamida, A.B., Chtourou, K.: CT scan contrast enhancement using singular value decomposition and adaptive gamma correction. Signal Image Video Process. 12(5), 905–913 (2018)

    Article  Google Scholar 

  17. Dai, P., Sheng, H., Zhang, J., Li, L., Wu, J., Fan, M.: Retinal fundus image enhancement using the normalized convolution and noise removing. Int. J. Biomed. Imaging 2016. Article ID 5075612 (2016)

  18. Zhou, M., Jin, K., Wang, S., Ye, J., Qian, D.: Color retinal image enhancement based on luminosity and contrast adjustment. IEEE Trans. Biomed. Eng. 65(3), 521–527 (2018)

    Article  Google Scholar 

  19. Rahman, S., Rahman, M.M., Abdullah-Al-Wadud, M., Al-Quaderi, G.D., Shoyaib, M.: An adaptive gamma correction for image enhancement. EURASIP J. Image Video Process. 35, 1–13 (2016)

    Google Scholar 

  20. Tiwari, M., Gupta, B.: Brightness preserving contrast enhancement of medical images using adaptive gamma correction and homomorphic filtering. In: IEEE Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), pp. 1–4. IEEE (2016)

  21. Chang, Y.T., Wang, J.T., Yang, W.H., Chen, X.W.: Contrast enhancement in palm bone image using quad-histogram equalization. In: 2014 International Symposium on Computer, Consumer and Control, pp. 1091–1094. IEEE (2014)

  22. He, L., Gao, X., Lu, W., Li, X., Tao, D.: Image quality assessment based on S-CIELAB model. Signal Image Video Process. 5(3), 283–90 (2011)

    Article  Google Scholar 

  23. Rezazadeh, S., Coulombe, S.: A novel discrete wavelet transform framework for full reference image quality assessment. Signal Image Video Process. 7(3), 559–73 (2013)

    Article  Google Scholar 

  24. Gopi, V.P., Palanisamy, P., Niwas, S.I.: Capsule endoscopic colour image denoising using complex wavelet transform. In: Venugopal, K.R., Patnaik, L.M. (eds.) Wireless Networks and Computational Intelligence, ICIP 2012, pp. 220–229. Springer, Berlin (2012)

    Chapter  Google Scholar 

  25. Gupta, S., Porwal, R.: Appropriate contrast enhancement measures for brain and breast cancer images. Int. J. Biomed. Imaging 2016. Article ID 4710842 (2016)

  26. Tunga, B., Koanaoullar, A.: Digital image decomposition and contrast enhancement using high-dimensional model representation. Signal Image Video Process. 12(2), 299–306 (2018)

    Article  Google Scholar 

  27. Maragatham, G., Roomi, S.M.M.: PSO-based stochastic resonance for automatic contrast enhancement of images. Signal Image Video Process. 10(1), 207–214 (2016)

    Article  Google Scholar 

  28. Agaian, S.S., Panetta, K., Grigoryan, A.M.: Transform-based image enhancement algorithms with performance measure. IEEE Trans. Image Process. 10(3), 367–82 (2001)

    Article  MATH  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Dr. Amjad Salman, ophthalmologist, and Ms. Latha B, optometrist of Joseph eye hospital, Tiruchirappalli, India, for providing the data and valuable suggestions. The authors also thank the MESSIDOR program partners for the data (refer www.adcis.net/en/DownloadThirdParty/Messidor.html).

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli, India

    Gopinath Palanisamy, Palanisamy Ponnusamy & Varun P. Gopi

Authors
  1. Gopinath Palanisamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Palanisamy Ponnusamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Varun P. Gopi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gopinath Palanisamy.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Palanisamy, G., Ponnusamy, P. & Gopi, V.P. An improved luminosity and contrast enhancement framework for feature preservation in color fundus images. SIViP 13, 719–726 (2019). https://doi.org/10.1007/s11760-018-1401-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-018-1401-y

Keywords

Search

Navigation