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68     CHAPTER 4  Retinal image preprocessing, enhancement, and registration




                         well-defined task, attempts to provide pertinent benchmarks are starting to appear,
                         as in Ref. [99] where existing datasets for RIR are compared and a benchmark
                         methodology is also proposed. Still, there is a clear need for new approaches to
                         benchmarking that will allow for more direct comparisons of methods and will
                         account for optical phenomena, such as optical distortions due to the eye lens,
                         vitreous humor, and chromatic aberrations.



                           Acknowledgment

                         The authors thank Polykarpos Karamaounas for technical assistance in the preparation
                         of the manuscript.



                           References

                           [1] M.  Matsui,  T.  Tashiro,  K.  Matsumoto,  S.  Yamamoto,  A  study  on  automatic  and
                             quantitative diagnosis of fundus photographs (Transl. from Japanese), Nippon Ganka
                             Gakkai Zasshi 77 (8) (1973) 907–918.
                           [2] A. Grosso, F. Veglio, M. Porta, F.M. Grignolo, T.Y. Wong, Hypertensive retinopathy
                             revisited: some answers, more questions, Br. J. Ophthalmol. 89 (12) (2005) 1646–1654,
                             https://doi.org/10.1136/bjo.2005.072546.
                           [3] R.P.  Danis, M.D.  Davis, Proliferative diabetic retinopathy, Diabetic Retinopathy,
                             Humana Press, Totowa, NJ, 2008, pp. 29–65.
                           [4] A. Chernomorets, A. Krylov, Blur detection in fundus images, International Conference
                             on BioMedical Engineering and Informatics, 2012, pp. 243–246.
                           [5] S. Mohammad, D. Morris, N. Thacker, Segmentation of optic disc in retina images using
                             texture, International Conference on Computer Vision Theory and Applications, 2014,
                             pp. 293–300.
                           [6] A.  Fleming, S.  Philip, K.  Goatman, J.  Olson, P.  Sharp, Automated microaneurysm
                             detection using local contrast normalization and local vessel detection, IEEE Trans.
                             Med. Imaging 25 (9) (2006) 1223–1232.
                           [7] T. Walter, P. Massin, A. Erginay, R. Ordonez, C. Jeulin, J. Klein, Automatic detection of
                             microaneurysms in color fundus images, Med. Image Anal. 11 (6) (2007) 555–566.
                           [8] A. Pujitha, G. Jahnavi, J. Sivaswamy, Detection of neovascularization in retinal images
                             using semi-supervised learning, IEEE International Symposium on Biomedical Imaging,
                             2017, pp. 688–691.
                           [9] A. Deshmukh, T. Patil, S. Patankar, J. Kulkarni, Features based classification of hard
                             exudates  in retinal  images,  International  Conference  on  Advances  in  Computing,
                             Communications and Informatics, 2015, pp. 1652–1655.
                           [10] N. Salem, A. Nandi, Novel and adaptive contribution of the red channel in pre- processing
                             of colour fundus images, J. Frankl. Inst. 344 (3) (2007) 243–256.
                           [11] C. Liu, M. Chang, Y. Chaung, S. Yu, A novel retinal image color texture enhancement
                             method based on multi-regression analysis, International Symposium on Computer,
                             Consumer and Control, 2016, pp. 793–796.
                           [12] J. Zhang, B. Dashtbozorg, E. Bekkers, J. Pluim, R. Duits, B.M. ter Haar Romeny, Robust
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