Abstract
Retinal fundus images are non-invasively acquired and hence, degraded in quality by inconsistent contrast, noise, varying illumination and hue. These visual complexities obstruct Ophthalmologists from accurate and timely analysis and diagnosis of eye-related diseases at an early stage to prevent premature loss of vision and sudden blindness. Several enhancement techniques have been proposed, but many of the enhanced images suffer the loss of information, colour imbalance and distortion. To this end, this paper presents contrast and luminous enhancement technique, including pitfalls to avoid to prevent further image degradation instead of improvement. The proposed method is evaluated on the DRIVE dataset, and the performance is measured by the statistical Intersection, Correlation and Chi-Square distance functions.
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References
Buhrmester, V., Münch, D., Bulatov, D., Arens, M.: Evaluating the impact of color information in deep neural networks. In: Morales, A., Fierrez, J., Sánchez, J.S., Ribeiro, B. (eds.) IbPRIA 2019. LNCS, vol. 11867, pp. 302–316. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-31332-6_27
WHO Team: World Report on Vision; WHO. Geneva, Switzerland, p. 180 (2019). ISBN 978-92-4-151657-0
Sule, O., Viriri, S.: Enhanced convolutional neural networks for segmentation of retinal blood vessel image. In: 2020 Conference on Information Communications Technology and Society (ICTAS), pp. 1–6. IEEE (2020)
Lian, S., Li, L., Lian, G., Xiao, X., Luo, Z., Li, S.: A global and local enhanced residual U-Net for accurate retinal vessel segmentation IEEE/ACM Trans. Comput. Biol. Bioinf. 18(3), 852–862 (2021). https://doi.org/10.1109/TCBB.2019.2917188
Palanisamy, G., Shankar, N.B., Ponnusamy, P., Gopi, V.P.: A hybrid feature preservation technique based on luminosity and edge based contrast enhancement in color fundus images. Biocybern. Biomed. Eng., 752–763 (2020)
Sahu, S., Singh, A.K., Ghrera, S.P., Elhoseny, M.: An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE. Optics Laser Technol. 110, 87–98 (2019)
Qureshi, I., Ma, J., Shaheed, K.: A hybrid proposed fundus image enhancement framework for diabetic retinopathy. Algorithms 1(12), 14. Multidisciplinary Digital Publishing Institute (2019)
Ab Rahim, H., Ibrahim, A.S., Zaki, W.M.D.W., Hussain, A.: Methods to enhance digital fundus image for diabetic retinopathy detection. In: 2014 IEEE 10th International Colloquium on Signal Processing and its Applications, pp. 221–224. CSPA IEEE (2014)
Bora, D.J.: Importance of image enhancement techniques in color image segmentation: A comprehensive and comparative study. arXiv preprint arXiv:1708.05081 (2017)
Indumathi, G., Sathananthavathi, V.: Chapter 5-Microaneurysms detection for early diagnosis of diabetic retinopathy using shape and steerable gaussian features. In: Jude, H.D., Balas, V.E. (eds.) Telemedicine Technologies, pp. 57–69. Academic Press, Cambridge (2019)
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(3), 750–759 (2021). https://doi.org/10.1007/s10278-021-00447-0
Hoover, AD. Kouznetsova, V. and Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3), 203–210. IEEE (2000)
Chavolla, E., Zaldivar, D., Cuevas, E., Perez, M.A.: Color spaces advantages and disadvantages in image color clustering segmentation. In: Hassanien, A.E., Oliva, D.A. (eds.) Advances in Soft Computing and Machine Learning in Image Processing. SCI, vol. 730, pp. 3–22. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-63754-9_1
Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Graph. Appl. 21(5), 34–41. IEEE (2001)
Nallaperumal, K., et al.: An analysis of suitable color space for visually plausible shadow-free scene reconstruction from single image. In: 2013 IEEE International Conference on Computational Intelligence and Computing Research, pp. 1–5. IEEE (2013)
Bora, D.J., Gupta, A.K.: A new efficient color image segmentation approach based on combination of histogram equalization with watershed algorithm. Int. J. Comput. Sci. Eng. 4(6), 156–167 (2016)
Yussof, W., Hitam, M.S., Awalludin, E.A., Bachok, Z.: Performing contrast limited adaptive histogram equalization technique on combined color models for underwater image enhancement. Int. J. Interact. Digit. Med. 1(1), 1–6 (2013)
Marín-Reyes, P.A., Lorenzo-Navarro, J., Castrillón-Santana, M.: Comparative study of histogram distance measures for re-identification. arXiv preprint arXiv:1611.08134 (2016)
Zhang, Q., Canosa, R.L.: A comparison of histogram distance metrics for content-based image retrieval. In: Imaging and Multimedia Analytics in a Web and Mobile World 2014 International Society for Optics and Photonics, p. 902700 (2014)
De Lima, J.R., Boff, F.A., De Souza Medeiros, A.C., Falate, R.: Histogram comparison using intersection metric applied to digital image analysis. Iberoamerican J. Appl. Comput. 2(1), 11–18 (2012)
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Sule, O.O. (2022). Analysis of Contrast and Luminous Enhancement Algorithms on Colour Retinal Fundus Images. In: Abraham, A., et al. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). SoCPaR 2021. Lecture Notes in Networks and Systems, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-96302-6_38
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DOI: https://doi.org/10.1007/978-3-030-96302-6_38
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