Abstract
Retinal imaging is used to diagnose common eye diseases. But retinal images that suffer from image blurring, uneven illumination and low contrast become useless for further diagnosis by automated systems. In this work, we have proposed a new method for overall contrast enhancement of the color retinal images. Initially, a gain matrix of luminance values which is obtained by adaptive gamma correction method is used to enhance all three color channels of the images. After that quantile-based histogram equalization is used to enhance overall visibility of the images. Enhancement results of the proposed method are compared with several other existing methods. Performance of the proposed method is evaluated on all images of publicly available Messidor database. Based on the assessment measure we have shown that the proposed method is able to enhance the contrast of given color retinal image without changing its structural information. The proposed technique is appeared to accomplish superior image enhancement with sufficient contrast enhancement, these enhancement results are better than other related techniques. This technique for color retinal image enhancement might be utilized to help ophthalmologists in the more productive screening of retinal ailments, what’s more, being developed of enhanced robotized image examination for clinical finding.
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Abramoff, M. D., Garvin, M. K., & Sonka, M. (2010). Retinal imaging and image analysis. IEEE Reviews in Biomedical Engineering, 3, 169–208.
Chen, B., Chen, Y., Shao, Z., Tongd, T., & Luo, L. (2016). Blood vessel enhancement via multi-dictionary and sparse coding: Application to retinal vessel enhancing. Neurocomputing, 200, 110–117.
Daniel, E. (2015). Optimum green plane masking for the contrast enhancement of retinal images using enhanced genetic algorithm. Optik, 126(18), 1726–1730.
Decenciere, E., et al. (2014). Feedback on a publicly distributed image database: The Messidor database. Image Analysis & Stereology, 33(3), 231–234.
Fenga, P., Pana, Y., Weia, B., Jin, W., & Mi, D. (2007). Enhancing retinal image by the Contourlet transform. Pattern Recogniton Letters, 28(4), 516–522.
Foracchia, M., Grisan, E., & Ruggeri, A. (2005). Luminosity and contrast normalization in retinal images. Medical Image Analysis, 9(3), 179–190.
GeethaRamani, R., & Balasubramanian, L. (2016). Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis. Biocybernetics and Biomedical Engineering, 36(1), 102–118.
Gupta, B., & Agarwal, T. K. (2017). Linearly quantile separated weighted dynamic histogram equalization for contrast enhancement. Computers & Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2017.01.010
Gupta, B., & Tiwari, M. (2015). Minimum mean brightness error contrast enhancement of color images using adaptive gamma correction with color preserving framework. International Journal for Light and Electron Optics, 127, 1671–1676.
Hani, A. F. M., & Nugroho, H. A. (2009). Retinal vasculature enhancement using independent component analysis. Journal of Biomedical Science and Engineering, 2(7), 543–549.
Korifi, R., Dreau, Y. L., Antinelli, J. F., Valls, R., & Dupuy, N. (2013). \(CIEL*a*b*\) color space predictive models for colorimetry devices—Analysis of perfume quality. Talanta, 104, 58–66.
Liao, M., Zhao, Y., Wang, X., & Dai, P. (2014). Retinal vessel enhancement based on multi-scale top-hat transformation and histogram fitting stretching. Optics & Laser Technology, 58, 56–62.
Mookiaha, M. R. K., Acharya, U. R., Chuaa, C. K., Lim, C. M., Ng, E. Y. K., & Laude, A. (2013). Computer-aided diagnosis of diabetic retinopathy: A review. Computers in Biology and Medicine, 43(12), 2136–2155.
Naik, S. K., & Murthy, C. A. (2003). Hue-preserving color image enhancement without gamut problem. IEEE Transactions on Image Processing, 12(12), 1591–1598.
Paulus, J., Meier, J., Bock, R., Hornegger, J., & Michelson, G. (2010). Automated quality assessment of retinal fundus photos. International Journal of Computer Assisted Radiology and Surgery, 5(6), 557–564.
Pisano, E. D., Zong, S., Hemminger, B. M., DeLuca, M., Johnston, R. E., Muller, K., et al. (1998). Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. Journal of Digital Imaging, 11(4), 193–200.
Ramluguna, G. S., Nagarajana, V. K., & Chakraborty, C. (2012). Small retinal vessels extraction towards proliferative diabetic retinopathy screening. Expert Systems with Applications, 39(1), 1141–1146.
Seoud, L., Hurtut, T., Chelbi, J., Cheriet, F., & Langlois, J. M. P. (2016). Red lesion detection using dynamic shape features for diabetic retinopathy screening. IEEE Transactions on Medical Imaging, 35(4), 1116–1126. https://doi.org/10.1109/TMI.2015.2509785.
Sevik, U., Kose, C., Berber, T., & Erdol, H. (2014). Identification of suitable fundus images using automated quality assessment methods. Journal of Biomedical Optics, 19(4), 046006.
Somkuwar, A. C., Patil, T. G., Patankar, S. S., Kulkarni, J. V. (2015). Intensity features based classification of hard exudates in retinal images. In 2015 annual IEEE India conference (INDICON), New Delhi (pp. 1–5). https://doi.org/10.1109/INDICON.2015.7443402.
Tang, H., & Zhao, Y. (2013). Edge detection in CIE \(L*a*b\) based on fractional differential. Journal of Image Graph., 18(6), 628–636.
Tiwari, M., Gupta, B., & Shrivastava, M. (2014). High speed quantile based histogram equalization for brightness preservation and contrast enhancement. IET Image Processing, 9(1), 80–89.
Wang, S., Zheng, J., Hu, H. M., & Li, B. (2013). Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Transactions on Image Processing, 22(9), 3538–3548.
Wu, X., Dai, B., & Bu, W. (2016). Optic disc localization using directional models. IEEE Transactions on Image Processing, 25(9), 4433–4442. https://doi.org/10.1109/TIP.2016.2590838.
Zhao, Y., Liu, Y., Wu, X., Harding, S. P., & Zheng, Y. (2015). Retinal vessel segmentation: An efficient graph cut approach with retinex and local phase. Plos One, 10(4), e0122332.
Zhou, M., Jin, K., Wang, S., Ye, J., & Qian, D. (2018). Color retinal image enhancement based on luminosity and contrast adjustment. IEEE Transactions on Biomedical Engineering, 99, 1.
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Authors thank (Decenciere 2014) for providing free access of Messidor database.
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Gupta, B., Tiwari, M. Color retinal image enhancement using luminosity and quantile based contrast enhancement. Multidim Syst Sign Process 30, 1829–1837 (2019). https://doi.org/10.1007/s11045-019-00630-1
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DOI: https://doi.org/10.1007/s11045-019-00630-1