Skip to main content
Log in

An adaptive enhancement and fovea detection technique for color fundus image analysis

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

Abstract

Accurate diagnosis of various retinal diseases requires high quality fundus images and exact fovea centre for pathological analysis. In this paper, a suitable preprocessing technique to enhance the fundus images and an accurate method for fovea centre detection are proposed. Luminosity component is enhanced by combining gamma correction, discrete shearlet transform and singular value decomposition. Local contrast is improved by applying CLAHE and a suitable weighting function is applied to alleviate over-enhancement. Region of interest for fovea localization is determined based on the optic disc position using the luminosity channel of the enhanced fundus image. This method is also suitable for images with abnormal structures around macula as the actual macula is identified from the multiple macula candidates based on optic disc position as well as the segmented blood vessels. Using appropriate color channels, thresholding and morphological operations, the macula is binary segmented and the fovea centre is marked. The proposed enhancement technique yields better results based on visual assessment as well as various quantitative parameters. The proposed method achieves the success rate of 99.4%, 100%, 98.9%, 99.2% and 100% for the proprietary, DRIVE, MESSIDOR, DIARETDB0 and DIARETDB1 databases, respectively.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. 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: 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp 1–8 (2016)

  2. 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. (2016). https://doi.org/10.1186/s13640-016-0138-1

    Article  Google Scholar 

  3. Chien, S., Chang, F., Hua, K., Chen, I., Chen, Y.: Contrast enhancement by using global and local histogram information jointly. In: 2017 International Conference on Advanced Robotics and Intelligent Systems (ARIS), pp 75–75 (2017) https://doi.org/10.1109/ARIS.2017.8297188

  4. Wang, X., Chen, L.: Contrast enhancement using feature-preserving bi-histogram equalization. Signal Image Video Process. 12(4), 685–692 (2018). https://doi.org/10.1007/s11760-017-1208-2

    Article  MathSciNet  Google Scholar 

  5. 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). https://doi.org/10.1109/TBME.2017.2700627

    Article  Google Scholar 

  6. Palanisamy, G., Ponnusamy, P., Gopi, V.P.: An improved luminosity and contrast enhancement framework for feature preservation in color fundus images. Signal Image Video Process. 13(4), 719–726 (2019). https://doi.org/10.1007/s11760-018-1401-y

    Article  Google Scholar 

  7. Mookiah, M.R.K., Acharya, U.R., Chua, K., Lim, C., Ng, E., Laude, A.: Computer-aided diagnosis of diabetic retinopathy: a review. Comput. Biol. Med. 43, 2136–2155 (2013). https://doi.org/10.1016/j.compbiomed.2013.10.007

    Article  Google Scholar 

  8. Niemeijer, M., Abramoff, M., Ginneken, B.: Fast detection of the optic disc and fovea in color fundus photographs. Med. Image Anal. 13, 859–870 (2009). https://doi.org/10.1016/j.media.2009.08.003

    Article  Google Scholar 

  9. Mohd Hani, A.F., Izhar, L., Nugroho, H.A.: Determination of foveal avascular zone in diabetic retinopathy digital fundus images. Comput. Biol. Med. 40, 657–64 (2010). https://doi.org/10.1016/j.compbiomed.2010.05.004

    Article  Google Scholar 

  10. Welfer, D., Scharcanski, J., Marinho, D.: Fovea center detection based on the retina anatomy and mathematical morphology. Comput. Methods Programs Biomed. 104(3), 397–409 (2011). https://doi.org/10.1016/j.cmpb.2010.07.006

    Article  Google Scholar 

  11. Hajeb, S., Rabbani, H., Akhlaghi, M.: A new combined method based on curvelet transform and morphological operators for automatic detection of foveal avascular zone. Signal Image Video Process. (2014). https://doi.org/10.1007/s11760-013-0530-6

    Article  Google Scholar 

  12. Qureshi, R., Kovacs, L., Harangi, B., Nagy, B., Petö, T., Hajdu, A.: Combining algorithms for automatic detection of optic disc and macula in fundus images. Comput. Vis. Image Underst. 116, 138–145 (2012). https://doi.org/10.1016/j.cviu.2011.09.001

    Article  Google Scholar 

  13. Kao, E.F., Lin, P.C., Chou, M.C., Jaw, T.S., Liu, G.C.: Automated detection of fovea in fundus images based on vessel-free zone and adaptive gaussian template. Comput. Methods Programs Biomed. 117, 92–103 (2014). https://doi.org/10.1016/j.cmpb.2014.08.003

    Article  Google Scholar 

  14. Mittal, G., Sivaswamy, J.: Optic disk and macula detection from retinal images using generalized motion pattern. In: 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp 1–4, (2015). https://doi.org/10.1109/NCVPRIPG.2015.7490071

  15. Medhi, J., Dandapat, S.: An effective fovea detection and automatic assessment of diabetic maculopathy in color fundus images. Comput. Biol. Med. 74, 30–44 (2016). https://doi.org/10.1016/j.compbiomed.2016.04.007

    Article  Google Scholar 

  16. Molina-Casado, J., Carmona, E., García-Feijoó, J.: Fast detection of the main anatomical structures in digital retinal images based on intra- and inter-structure relational knowledge. Comput. Methods Programs Biomed. 149, 55–68 (2017). https://doi.org/10.1016/j.cmpb.2017.06.022

    Article  Google Scholar 

  17. Tan, J.H., Acharya, U.R., Bhandary, S., Chua, K., Sivaprasad, S.: Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. J. Comput. Sci. 20, 70–79 (2017). https://doi.org/10.1016/j.jocs.2017.02.006

    Article  Google Scholar 

  18. Al-Bander, B., Al-Nuaimy, W., Williams, B., Zheng, Y.: Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc. Biomed. Signal Process. Control 40, 91–101 (2018). https://doi.org/10.1016/j.bspc.2017.09.008

    Article  Google Scholar 

  19. Tagore, M.R.N., Babu, K.G., Saradhi, M.P., Reddy, P.A.: Fovea localization in digital retinal images. Int. J. Innov. Technol. Explor. Eng. 8, 350–354 (2019)

    Google Scholar 

  20. Chalakkal, R.J.: Automatic detection and segmentation of optic disc and fovea in retinal images. IET Image Process. 12, 2100–2110 (2018)

    Article  Google Scholar 

  21. Diwakar, M., Lamba, S., Gupta, H.: Ct image denoising based on thresholding in shearlet domain. Biomed. Pharmacol. J. 11, 671–677 (2018)

    Article  Google Scholar 

  22. G Easley WL, D Labate.: (Accessed 27 Nov 2019) Software and demo. https://www.math.uh.edu/~dlabate/software.html

  23. Dehghani, A., Moghaddam, H., Moin, M.S.: Optic disc localization in retinal images using histogram matching. EURASIP J. Image Video Process. (2012). https://doi.org/10.1186/1687-5281-2012-19

    Article  Google Scholar 

  24. Razmjooy, N., Mousavi, B., Khalilpour, M., Hosseini, H.: Automatic selection and fusion of color spaces for image thresholding. Signal Image Video Process. (2014). https://doi.org/10.1007/s11760-012-0303-7

    Article  Google Scholar 

  25. Wang, Zhou, Bovik, A.C.: A universal image quality index. IEEE Signal Process. Lett. 9(3), 81–84 (2002). https://doi.org/10.1109/97.995823

    Article  Google Scholar 

  26. Gupta, V., Mittal, M., Mittal, V., Gupta, A.: Ecg signal analysis using cwt, spectrogram and autoregressive technique. Iran J. Comput, Sci. 4, 265–280 (2021). https://doi.org/10.1007/s42044-021-00080-8

    Article  Google Scholar 

  27. Gupta, V., Mittal, M., Mittal, V.: Detection of r-peaks using fractional fourier transform and principal component analysis. J. Ambient Intell. Humaniz. Comput. 13, 961–972 (2022). https://doi.org/10.1007/s12652-021-03484-3

    Article  Google Scholar 

  28. Gupta, V., Mittal, M.: Qrs complex detection using stft, chaos analysis, and pca in standard and real-time ecg databases. J. Inst. Eng. (India) Ser. B 100, 489–497 (2019). https://doi.org/10.1007/s40031-019-00398-9

  29. Gomaa, A., Minematsu, T., Abdelwahab, M., Abo-Zahhad, M., Taniguchi, R.I.: Faster cnn-based vehicle detection and counting strategy for fixed camera scenes. Multimedia Tools Appl. (2022). https://doi.org/10.1007/s11042-022-12370-9

    Article  Google Scholar 

  30. Gomaa, A., Abdelwahab, M., Abo-Zahhad, M.: Efficient vehicle detection and tracking strategy in aerial videos by employing morphological operations and feature points motion analysis. Multimedia Tools Appl. (2020). https://doi.org/10.1007/s11042-020-09242-5

    Article  Google Scholar 

Download references

Acknowledgements

The authors wish to express their gratitude to Dr. Amjad Salman, Joseph Eye Hospital, Trichy, India for providing the required data.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Palanisamy Ponnusamy.

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 adaptive enhancement and fovea detection technique for color fundus image analysis. SIViP 17, 831–838 (2023). https://doi.org/10.1007/s11760-022-02295-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-022-02295-z

Keywords

Navigation