Abstract
Retinal images show an essential role in Ophthalmology to diagnosis wide set of diseases. In this direction, using retinal images in computerized techniques increases the ability of diagnosis in fast time effectively. However, some eye diseases and capturing conditions produce low-quality retinal images, which reduces the diagnosis ability for machines and humans. To solve that, several works have been proposed to enhance retinal images. But they show a lot of negative observations, especially with color images of retina. In this paper, a novel enhancement algorithm for color retinal images is proposed. It consists of three stages; firstly, the appearance of visual details is increased by enhancing the contrast of structural details of retinal image using details enhanced and Bilateral filters. Then, a novel uneven illumination correction method is proposed to solve the uneven illumination problem adaptively. Finally, the advantages of both previous stages are combined using HSV color model to produce the final enhanced retinal images. DRIVE and STARE benchmark datasets are used to conduct experiments. The results were compared with histogram Equalized (HE), Contrast Stretching (CS), the adaptive histogram equalization (CLAHE) and Zhou’s method retinal enhancement methods. In conclusion, the results show that the proposed method shows high performance compared with the corresponding enhancement methods.
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Mvoulana, A.; Kachouri, R.; Akil, M.: Fully automated method for glaucoma screening using robust optic nerve head detection and unsupervised segmentation based cup-to-disc ratio computation in retinal fundus images. Comput. Med. Imaging Gr. 77, 101643 (2019)
Mitra, A.; Roy, S.; Roy, S.; Setua, S.K.: Enhancement and restoration of non-uniform illuminated fundus image of retina obtained through thin layer of cataract. Comput. Methods Progr. Biomed. 156, 169–178 (2018)
Almotiri, J.; Elleithy, K.; Elleithy, A.: Retinal vessels segmentation techniques and algorithms: a survey. Appl. Sci. 8, 155 (2018)
Zhu, C.; Zou, B.; Zhao, R.; Cui, J.; Duan, X.; Chen, Z.; Liang, Y.: Retinal vessel segmentation in colour fundus images using extreme learning machine. Comput. Med. Imaging Gr. 55, 68–77 (2017)
Can, A.; Stewart, C.V.; Roysam, B.; Tanenbaum, H.L.: A feature-based, robust, hierarchical algorithm for registering pairs of images of the curved human retina. IEEE Trans. Pattern Anal. Mach. Intell. 24, 347–364 (2002)
Hsu, W.Y.; Chou, C.Y.: Medical image enhancement using modified color histogram equalization. J. Med. Biol. Eng. 35, 580–584 (2015)
Wong, T.Y.; Loon, S.C.; Saw, S.M.: The epidemiology of age related eye diseases in Asia. Br. J. Ophthalmol. 90, 506–511 (2006)
Kälviäinen, R.; Uusitalo, H.: DIARETDB1 diabetic retinopathy database and evaluation protocol. In: Medical Image Understanding and Analysis. p. 61. Citeseer (2007)
Reddy, P.S.; Singh, H.; Kumar, A.; Balyan, L.K.; Lee, H.-N.: Retinal fundus image enhancement using piecewise gamma corrected dominant orientation based histogram equalization. In: 2018 International Conference on Communication and Signal Processing (ICCSP). pp. 124–128. IEEE (2018)
Tekalp, A.M.: Digital Video Processing. Prentice Hall Press, Cambridge (2015)
Burger, W.; Burge, M.J.: Digital Image Processing: An Algorithmic Introduction Using Java. Springer, Berlin (2009)
Joshi, G.D.; Sivaswamy, J.: Colour retinal image enhancement based on domain knowledge. In: 2008 Sixth Indian Conference on Computer Vision, Graphics and Image Processing, pp. 591–598 (2008)
Jain, K.; Arya, I.B.: A survey of contrast enhancement technique for remote sensing images. Int. J. Electr. Electron. Comput. Eng. 3, 1 (2014)
Raj, A.; Tiwari, A.K.; Martini, M.G.: Fundus image quality assessment: survey, challenges, and future scope. IET Image Process. 13, 1211–1224 (2019)
Rashmi Choudhary, S.G.: Survey on Image Contrast Enhancement Techniques. Int. J. Innov. Stud. Sci. Eng. Technol. 2, 21–25 (2016)
Westerweel, J.: Fundamentals of digital particle image velocimetry. Meas. Sci. Technol. 8, 1379 (1997)
Yuan, X.; Gu, L.; Chen, T.; Elhoseny, M.; Wang, W.: A fast and accurate retina image verification method based on structure similarity. In: 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications (BigDataService). pp. 181–185 (2018)
Liao, M.; Zhao, Y.Q.; Wang, X.H.; Dai, P.S.: Retinal vessel enhancement based on multi-scale top-hat transformation and histogram fitting stretching. Opt. Laser Technol. 58, 56–62 (2014)
Haldar, R.; Aruchamy, S.; Chatterjee, A.; Bhattacharjee, P.: Diabetic retinopathy image enhancement using vessel extraction in retinal fundus images by programming in raspberry pi controller board. In: 2016 International Conference on Inter Disciplinary Research in Engineering and Technology. p. 37 (2016)
Xiong, L.; Li, H.; Xu, L.: An enhancement method for color retinal images based on image formation model. Comput. Methods Prog. Biomed. 143, 137–150 (2017)
Agarwal, M.; Mahajan, R.: Medical image contrast enhancement using range limited weighted histogram equalization. Procedia Comput. Sci. 125, 149–156 (2018)
Qureshi, I.; Ma, J.; Abbas, Q.: Recent development on detection methods for the diagnosis of diabetic retinopathy. Symmetry (Basel) 11, 749 (2019)
Qureshi, I.; Khan, M.A.; Sharif, M.; Saba, T.; Ma, J.: Detection of glaucoma based on cup-to-disc ratio using fundus images. Int. J. Intell. Syst. Technol. Appl. 19, 1–16 (2020)
Stitt, A.W.; Curtis, T.M.; Chen, M.; Medina, R.J.; McKay, G.J.; Jenkins, A.; Gardiner, T.A.; Lyons, T.J.; Hammes, H.-P.; Simo, R.: The progress in understanding and treatment of diabetic retinopathy. Prog. Retin. Eye Res. 51, 156–186 (2016)
Srinidhi, C.L.; Aparna, P.; Rajan, J.: A visual attention guided unsupervised feature learning for robust vessel delineation in retinal images. Biomed. Signal Process. Control. 44, 110–126 (2018)
Qureshi, I.; Ma, J.; Shaheed, K.: A hybrid proposed fundus image enhancement framework for diabetic retinopathy. Algorithms 12, 14 (2019)
Chen, B.; Chen, Y.; Shao, Z.; Tong, T.; Luo, L.: Blood vessel enhancement via multi-dictionary and sparse coding: application to retinal vessel enhancing. Neurocomputing. 200, 110–117 (2016)
Pizer, S.M.; Amburn, E.P.; Austin, J.D.; Cromartie, R.; Geselowitz, A.; Greer, T.; ter Haar Romeny, B.; Zimmerman, J.B.; Zuiderveld, K.: Adaptive histogram equalization and its variations. Comput. Vis. Gr. Image Process. 39, 355–368 (1987)
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, 521–527 (2017)
Gastal, E.S.L.; Oliveira, M.M.: Domain transform for edge-aware image and video processing. In: ACM SIGGRAPH 2011 Papers. pp. 1–12 (2011)
Gastal, E.S.L.: Efficient high-dimensional filtering for image and video processing (2015)
Noor, A.I.; Mokhtar, M.H.; Rafiqul, Z.K.; Pramod, K.M.: Understanding color models: a review. ARPN J. Sci. Technol. 2, 265–275 (2012)
Cardani, D.: Adventures in HSV Space. Lab. Robótica, Inst. Tecnológico Autónomo México, Mexico City (2001)
Hoover, A.; Goldbaum, M.: Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans. Med. Imaging. 22, 951–958 (2003). https://doi.org/10.1109/TMI.2003.815900
Staal, J.; Abràmoff, M.D.; Niemeijer, M.; Viergever, M.A.; Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging. 23, 501–509 (2004)
Akram, M.U.; Atzaz, A.; Aneeque, S.F.; Khan, S.A.: Blood vessel enhancement and segmentation using wavelet transform. In: 2009 International Conference on Digital Image Processing. pp. 34–38. IEEE (2009)
Noronha, K.; Nayak, J.; Bhat, S.N.: Enhancement of retinal fundus image to highlight the features for detection of abnormal eyes. In: TENCON 2006–2006 IEEE Region 10 Conference. pp. 1–4. IEEE (2006)
Javed, M.; Nagabhushan, P.; Chaudhuri, B.B.; Singh, S.K.: Edge based enhancement of retinal images using an efficient JPEG-compressed domain technique. J. Intell. Fuzzy Syst. 36, 541–556 (2019)
Singh, H.; Kumar, A.; Balyan, L.K.; Lee, H.-N.: Fractional-order integration based fusion model for piecewise gamma correction along with textural improvement for satellite images. IEEE Access 7, 37192–37210 (2019)
Singh, H.; Kumar, A.; Balyan, L.K.; Lee, H.-N.: Optimally sectioned and successively reconstructed histogram sub-equalization based gamma correction for satellite image enhancement. Multimed. Tools Appl. 78, 20431–20463 (2019)
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Bataineh, B., Almotairi, K.H. Enhancement Method for Color Retinal Fundus Images Based on Structural Details and Illumination Improvements. Arab J Sci Eng 46, 8121–8135 (2021). https://doi.org/10.1007/s13369-021-05429-6
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DOI: https://doi.org/10.1007/s13369-021-05429-6