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Fundus image quality enhancement for blood vessel detection via a neural network using CLAHE and Wiener filter

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Abstract

Purpose

Blood vessel segmentation is the most important step for detecting changes in retinal vascular structures in retinal images. While these images are widely used in clinical diagnosis, they are generally degraded by noise and are limited by low contrast. In this paper, we address the problem of improving fundus image quality for blood vessel detection.

Methods

We used contrast limited adaptive histogram equalization (CLAHE) to improve contrast and the Wiener filter for noise reduction. A multilayer artificial neural network was used to optimize the values from CLAHE and the Wiener filter for blood vessel segmentation. Furthermore, several training and classification rounds were performed (3240, with 200 epochs each), using a combination of CLAHE and Wiener parameters and a fixed network configuration.

Results

The proposed methodology was tested in the DRIVE database, achieving accuracy, sensitivity, and specificity values of 0.9505, 0.7564, and 0.9696, respectively.

Conclusion

The results were encouraging for almost all metrics and comparable to those of state-of-the-art blood vessel segmentation processes. Therefore, the parameter set effectively improved the fundus image quality for blood vessel segmentation, relative to the classification. These results are important since the more precise the segmentation step is, the greater the chances are of building a robust and specialized diagnostic system.

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References

  1. Agurto C, Yu H, Murray V, Pattichis MS, Nemeth S, Barriga S, et al. A multiscale decomposition approach to detect abnormal vasculature in the optic disc. Comput Med Imaging Graph. 2015;43:137–49. https://doi.org/10.1016/j.compmedimag.2015.01.001.

  2. Azzopardi G, Strisciuglio N, Vento M, Petkov N. Trainable COSFIRE filters for vessel delineation with application to retinal images. Med Image Anal. 2015;19(1):46–57. https://doi.org/10.1016/j.media.2014.08.002.

  3. Bhattacharjee U, Das P. Performance evaluation of Wiener filter and Kalman filter combined with spectral subtraction in speaker verification system. Int J Innov Technol Explor Eng (IJITEE). 2013;2(2):108–12.

  4. Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M. Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans Med Imaging. 1989;8(3):263–9. https://doi.org/10.1109/42.34715.

  5. Dorion T. Manual de exame do fundo de olho. Barueri: Manole; 2002.

  6. Elbalaoui A, Fakir M, Taifi K, Merbouha A. Automatic detection of blood vessel in retinal images. In Proceedings - Computer Graphics, Imaging and Visualization: New Techniques and Trends, CGiV 2016. 2016. https://doi.org/10.1109/CGiV.2016.69.

  7. Fausett L. Fundamentals of neural networks: architectures, algorithms, and applications. Upper Saddle River: Prentice-Hall, Inc.; 1994.

  8. Forouzanfar MH, Afshin A, Alexander LT, Anderson HR, Bhutta ZA, Biryukov S, et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the global burden of disease study 2015. Lancet. 2017;388(10053):1659–724. https://doi.org/10.1016/S0140-6736(16)31679-8.

  9. Fraz MM, Barman SA, Remagnino P, Hoppe A, Basit A, Uyyanonvara B, et al. An approach to localize the retinal blood vessels using bit planes and centerline detection. Comput Methods Prog Biomed. 2012;108(2):600–16. https://doi.org/10.1016/j.cmpb.2011.08.009.

  10. GeethaRamani R, Balasubramanian L. Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis. Biocybern Biomed Eng. 2016;36(1):102–18. https://doi.org/10.1016/j.bbe.2015.06.004.

  11. Ghael SP, Sayeed AM, Baraniuk RG. Improved wavelet denoising via empirical Wiener filtering. Proc SPIE. 1997:389–99. https://doi.org/10.1117/12.292799.

  12. Gonzalez RC, Woods RE. Processamento Digital de Imagens. 3rd ed. São Paulo: Pearson Prentice Hall; 2010.

  13. Hassanien AE, Emary E, Zawbaa HM. Retinal blood vessel localization approach based on bee colony swarm optimization, fuzzy c-means and pattern search. J Vis Commun Image Represent. 2015;31:186–96. https://doi.org/10.1016/j.jvcir.2015.06.019.

  14. Kumar K, Rani B, Khan H, Ravi T. Detection of retinal diseases by tracing vessel trees and accurately segmenting vessels. Int J Eng. 2012;4:182–7 Retrieved from http://www.doaj.org/doaj?func=abstract&id=1028907.

  15. Kumar S, Choudhary S, Gupta R, Kumar B. Performance evaluation of joint filtering and histogram equalization techniques for retinal fundus image enhancement. 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2018. 2018;1–5. https://doi.org/10.1109/UPCON.2018.8597050.

  16. 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. 2015;9(10):908–15. https://doi.org/10.1049/iet-ipr.2015.0150.

  17. Liskowski P, Krawiec K. Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging. 2016;35(11):2369–80. https://doi.org/10.1109/TMI.2016.2546227.

  18. Ma J, Fan X, Yang SX, Zhang X, Zhu X. Contrast limited adaptive histogram equalization based fusion for underwater image enhancement. preprints. 2017;(March):1–27. https://doi.org/10.20944/preprints201703.0086.v1

  19. Maji D, Santara A, Mitra P, Sheet D. Ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images. 2016. Retrieved from http://arxiv.org/abs/1603.04833.

  20. Mookiah MRK, Acharya UR, Chua CK, Lim CM, Ng EYK, Laude A. Computer-aided diagnosis of diabetic retinopathy: a review. Comput Biol Med. 2013;43(12):2136–55. https://doi.org/10.1016/j.compbiomed.2013.10.007.

  21. Niemeijer M, Staal J, van Ginneken B, Loog M, Abramoff MD. Comparative study of retinal vessel segmentation methods on a new publicly available database. 2004;(May 2004):648. https://doi.org/10.1117/12.535349

  22. Orlando JI, Blaschko M. Learning Fully-Connected CRFs for Blood Vessel Segmentation in Retinal Images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2014;8673 LNCS:634–41. https://doi.org/10.1007/978-3-319-10404-1_79.

  23. Singh NP, Srivastava R. Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter. Comput Methods Prog Biomed. 2016;129:40–50. https://doi.org/10.1016/j.cmpb.2016.03.001.

  24. Sonali SS, Singh AK, Ghrera SP, Elhoseny M. An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE. Opt Laser Technol. 2019;110:87–98. https://doi.org/10.1016/j.optlastec.2018.06.061

  25. Soomro TA, Afifi AJ, Gao J, Hellwich O, Khan MAU, Paul M, et al. Boosting sensitivity of a retinal vessel segmentation algorithm. Pattern Anal Applic. 2017;22:583–99. https://doi.org/10.1007/s10044-017-0661-4.

  26. Sreng S, Maneerat N, Hamamoto K. Automated microaneurysms detection in fundus images using image segmentation. In 2017 International Conference on Digital Arts, Media and Technology (ICDAMT). IEEE. 2017;19–23. https://doi.org/10.1109/ICDAMT.2017.7904926.

  27. Suero A, Marin D, Gegundez-arias ME, Bravo JM. Locating the optic disc in retinal images using morphological techniques. Iwbbio. 2013;18–20.

  28. Vega R, Sanchez-Ante G, Falcon-Morales LE, Sossa H, Guevara E. Retinal vessel extraction using lattice neural networks with dendritic processing. Comput Biol Med. 2015;58:20–30. https://doi.org/10.1016/j.compbiomed.2014.12.016.

  29. Vostatek P, Claridge E, Uusitalo H, Hauta-Kasari M, Fält P, Lensu L. Performance comparison of publicly available retinal blood vessel segmentation methods. Comput Med Imaging Graph. 2016. https://doi.org/10.1016/j.compmedimag.2016.07.005.

  30. Xie C-H, Liu Y-J, Chang J-Y. Medical image segmentation using rough set and local polynomial regression. Multimed Tools Appl. 2015;74(6):1885–914. https://doi.org/10.1007/s11042-013-1723-2.

  31. Zaki WMDW, Zulkifley MA, Hussain A, Halim WHWA, Mustafa NBA, Ting LS. Diabetic retinopathy assessment: towards an automated system. Biomed Sign Process Control. 2016;24:72–82. https://doi.org/10.1016/j.bspc.2015.09.011.

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Author information

Correspondence to Cristiane de Fátima dos Santos Cardoso.

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Conflict of interest

Author Jucelino Cardoso Marciano dos Santos declares that he has no conflict of interest. Author Gilberto Arantes Carrijo declares that he has no conflict of interest. Author Cristiane de Fátima dos Santos Cardoso declares that she has no conflict of interest. Author Júlio César Ferreira declares that he has no conflict of interest. Author Pedro Moises Sousa declares that he has no conflict of interest. Author Ana Cláudia Patrocínio declares that she has no conflict of interest.

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dos Santos, J.C.M., Carrijo, G.A., de Fátima dos Santos Cardoso, C. et al. Fundus image quality enhancement for blood vessel detection via a neural network using CLAHE and Wiener filter. Res. Biomed. Eng. (2020). https://doi.org/10.1007/s42600-020-00046-y

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Keywords

  • CLAHE
  • Wiener filter
  • Blood vessel segmentation
  • Fundus image
  • Neural network