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A Hybrid Algorithm to Enhance Colour Retinal Fundus Images Using a Wiener Filter and CLAHE

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Abstract

Digital images used in the field of ophthalmology are among the most important methods for automatic detection of certain eye diseases. These processes include image enhancement as a primary step to assist optometrists in identifying diseases. Therefore, many algorithms and methods have been developed for the enhancement of retinal fundus images, which may experience challenges that typically accompany enhancement processes, such as artificial borders and dim lighting that mask image details. To eliminate these problems, a new algorithm is proposed in this paper based on separating colour images into three channels (red, green, and blue). The green channel is passed through a Wiener filter and reinforced using the CLAHE technique before merging with the original red and blue channels. Reducing the green channel noise with this approach is proven effective over the other colour channels. Results from the Contrast Improvement Index (CII) and linear index of fuzziness (r) test indicate the success of the proposed algorithm compared with alternate algorithms in the application of improving blood vessel imagery and other details within ten test fundus images selected from the DRIVER database.

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References

  1. Cense B, Chen TC, Park BH, Pierce MC, De Boer JF, et al: In vivo birefringence and thickness measurements of the human retinal nerve fiber layer using polarization-sensitive optical coherence tomography. Journal of biomedical optics, 9(1), 121-126,2004‏

    Article  Google Scholar 

  2. Gregory R: Eye and brain: the psychology of seeing (Vol. 80). Princeton university press.

  3. Joesch M, Meister M: A neuronal circuit for colour vision based on rod–cone opponency. Nature, 532(7598), 236-239,2016‏

    Article  CAS  Google Scholar 

  4. Krol J, Busskamp V, Markiewicz I, Stadler MB, Ribi S, Richter J, Oertner TG: Characterizing light-regulated retinal microRNAs reveals rapid turnover as a common property of neuronal microRNAs. Cell, 141(4), 618-631,2010‏

    Article  CAS  Google Scholar 

  5. Laha B, Stafford BK, Huberman AD, et al: Regenerating optic pathways from the eye to the brain. Science, 356(6342), 1031-1034,2017‏

    Article  CAS  Google Scholar 

  6. Shepherd RK, Shivdasani MN, Nayagam DA, Williams CE, Blamey PJ, et al: Visual prostheses for the blind. Trends in biotechnology, 31(10), 562-571,2013‏

    Article  CAS  Google Scholar 

  7. Abràmoff MD, Garvin MK, Sonka M, et al: Retinal imaging and image analysis. IEEE reviews in biomedical engineering, 3, 169-208,2010‏

    Article  Google Scholar 

  8. Schuerch K, Woods RL, Lee W, Duncker T, Delori FC, Allikmets R, Sparrow JR, et al: Quantifying fundus autofluorescence in patients with retinitis pigmentosa. Investigative ophthalmology & visual science, 58(3), 1843-1855,2017‏

    Article  Google Scholar 

  9. Dysli C, Schuerch K, Escher P, Wolf S, Zinkernagel MS et al: Fundus autofluorescence lifetime patterns in retinitis pigmentosa. Investigative ophthalmology & visual science, 59(5), 1769-1778,2018‏

    Article  CAS  Google Scholar 

  10. Yun WL, Acharya UR, Venkatesh YV, Chee C, Min LC, Ng EYK, et al: Identification of different stages of diabetic retinopathy using retinal optical images. Information sciences, 178(1), 106-121,2008‏

    Article  Google Scholar 

  11. Sahu S, Singh AK, Ghrera SP, Elhoseny M, et al: An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE. Optics & Laser Technology, 110, 87-98,2019‏

    Article  Google Scholar 

  12. Matkovic K, Neumann L, Neumann A, Psik T, Purgathofer W, et al: Global contrast factor-a new approach to image contrast. Computational Aesthetics. 159-168,2005‏

    Google Scholar 

  13. Alwazzan  J, Ismael  MA, Hussain  MK, et al: Brain tumour isolation in MRI images based on statistical properties and morphological process techniques. In Journal of Physics: Conference Series (Vol. 1279, No. 1, p. 012018). IOP Publishing,2019‏

  14. Banić N,  Lončarić S: Smart light random memory sprays Retinex: a fast Retinex implementation for high-quality brightness adjustment and color correction. JOSA A, 32(11), 2136-2147,2015‏

    Article  Google Scholar 

  15. Chiu K, Herf M, Shirley P, Swamy S, Wang C, Zimmerman K, et al: Spatially nonuniform scaling functions for high contrast images. In Graphics Interface (pp. 245–245). Canadian Information Processing Society,1993‏

  16. Setiawan AW, Mengko TR, Santoso OS, Suksmono AB, et al: Color retinal image enhancement using CLAHE. In International Conference on ICT for Smart Society (pp. 1–3). IEEE,2013‏

  17. Mitra A, Roy S, Roy S, Setua SK, et al: Enhancement and restoration of non-uniform illuminated fundus image of retina obtained through thin layer of cataract. Computer methods and programs in biomedicine, 156, 169-178,2018‏

    Article  Google Scholar 

  18. Dai P, Sheng H, Zhang J, Li L, Wu J, Fan M, et al: Retinal fundus image enhancement using the normalized convolution and noise removing. International journal of biomedical imaging, 2016.‏

    Google Scholar 

  19. Reza AM: Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. Journal of VLSI signal processing systems for signal, image and video technology, 38(1), 35-44,2004‏

    Article  Google Scholar 

  20. Foracchia M, Grisan E, Ruggeri A, et al: Luminosity and contrast normalization in retinal images. Medical image analysis, 9(3), 179-190,2005‏

    Article  Google Scholar 

  21. Joshi GD, Sivaswamy J: Colour retinal image enhancement based on domain knowledge. In 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing (pp. 591–598). IEEE,2008‏

  22. Qureshi I, Ma J, Shaheed K, et al: A hybrid proposed fundus image enhancement framework for diabetic retinopathy. Algorithms, 12(1), 14,2019+‏

    Article  Google Scholar 

  23. Li L, Si Y, Jia Z, et al: Medical image enhancement based on CLAHE and unsharp masking in NSCT domain. Journal of Medical Imaging and Health Informatics, 8(3), 431-438,2018‏

    Article  Google Scholar 

  24. Zhou M, Jin K, Wang S, Ye J, Qian D, et al:Color retinal image enhancement based on luminosity and contrast adjustment. IEEE Transactions on Biomedical Engineering, 65(3), 521-527,2017‏

    Article  Google Scholar 

  25. Celik T, Tjahjadi, T: Automatic image equalization and contrast enhancement using Gaussian mixture modeling. IEEE transactions on image processing, 21(1), 145-156,2011‏

    Article  Google Scholar 

  26. Wang Z, Wang K, Yang F, Pan S, Han Y, Zhao X, et al: Image enhancement for crop trait information acquisition system. Information Processing in Agriculture, 5(4), 433-442,2018‏

    Article  Google Scholar 

  27. Oh J, Hwang H: Feature enhancement of medical images using morphology-based homomorphic filter and differential evolution algorithm. International Journal of Control, Automation and Systems, 8(4), 857-861,2010‏

    Article  Google Scholar 

  28. Bai X, Zhou F, Xue B, et al: Image enhancement using multi scale image features extracted by top-hat transform. Optics & Laser Technology, 44(2), 328-336,2012‏

    Article  Google Scholar 

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Correspondence to Mohammed J. Alwazzan.

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Alwazzan, M.J., Ismael, M.A. & Ahmed, A.N. A Hybrid Algorithm to Enhance Colour Retinal Fundus Images Using a Wiener Filter and CLAHE. J Digit Imaging 34, 750–759 (2021). https://doi.org/10.1007/s10278-021-00447-0

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  • DOI: https://doi.org/10.1007/s10278-021-00447-0

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