Abstract
Retinal images provide a simple non-invasive method for the detection of several eye diseases. However, many factors can result in the degradation of the images’ quality, thus affecting the reliability of the performed diagnosis. Enhancement of retinal images is thus essential to increase the overall image quality. In this work, a wavelet-based retinal image enhancement algorithm is proposed that considers four different common quality issues within retinal images (1) noise removal, (2) sharpening, (3) contrast enhancement and (4) illumination enhancement. Noise removal and sharpening are performed by processing the wavelet detail subbands, such that the upper detail coefficients are eliminated, whereas bilinear mapping is used to enhance the lower detail coefficients based on their relevance. Contrast and illumination enhancement involve applying contrast limited adaptive histogram equalization (CLAHE) and the proposed luminance boosting method to the approximation subband, respectively. Four different retinal image quality measures are computed to assess the proposed algorithm and to compare its performance against four other methods from literature. The comparison showed that the introduced method resulted in the highest overall image improvement followed by spatial CLAHE for all the considered quality measures; thus, indicating the superiority of the proposed wavelet-based enhancement method.
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References
WHO: World Report on vision: executive summary (2019)
Abdel-Hamid, L., El-Rafei, A., El-Ramly, S., Michelson, G., Hornegger, J.: Retinal image quality assessment based on image clarity and content. J. Biomed. Opt. 21(9), 096007 (2016). https://doi.org/10.1117/1.JBO.21.9.096007
Youssif, A.A., Ghalwash, A.Z., Ghoneim, A.S.: Comparative study of contrast enhancement and illumination equalization methods for retinal vasculature segmentation. In: Cairo International Biomedical Engineering Conference, pp. 1–5 (2006)
Yao, Z., Zhang, Z., Xu, L.Q., Fan, Q., Xu, L.: Generic features for fundus image quality evaluation. In: IEEE 18th International Conference e-Health Networking, Applications and Services (Healthcom), pp. 1–6 (2016). https://doi.org/10.1109/HealthCom.2016.7749522
Yu, H., Agurto, C., Barriga, S., Nemeth, S.C., Soliz, P., Zamora, G.: Automated image quality evaluation of retinal fundus photographs in diabetic retinopathy screening. In: IEEE Southwest Symposium on Image Analysis Interpretation, pp. 125–128 (2012)
Setiawan, A.W., Mengko, T.R., Santoso, O.S., Suksmono, A.B.: Color retinal image enhancement using CLAHE. In: International Conference on ICT for Smart Society ICISS, pp. 215–217 (2013). https://doi.org/10.1109/ICTSS.2013.6588092
Ramasubramanian, B., Selvaperumal, S.: A comprehensive review on various preprocessing methods in detecting diabetic retinopathy. In: International Conference on Communication Signal Processing ICCSP, pp. 642–646 (2016). https://doi.org/10.1109/ICCSP.2016.7754220
Jintasuttisak, T., Intajag, S.: Color retinal image enhancement by Rayleigh contrast-limited adaptive histogram equalization. In: IEEE 14th International Conference on Control Automation and Systems (ICCAS), pp. 692–697 (2014)
Jin, K., et al.: Computer-aided diagnosis based on enhancement of degraded fundus photographs. Acta Ophthalmol. 96(3), e320–e326 (2018). https://doi.org/10.1111/aos.13573
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(3), 521–527 (2018). https://doi.org/10.1109/TBME.2017.2700627
Sonali, Sahu, S., Singh, A.K., Ghrera, S.P., Elhoseny, M.: An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE. Opt. Laser Technol. 110(3), 87–98 (2019). https://doi.org/10.1016/j.optlastec.2018.06.061
Ninassi, A., Le Meur, O., Le Callet, P., Barba, D.: On the performance of human visual system based image quality assessment metric using wavelet domain. In: SPIE Human Vision Electronic Imaging XIII, vol. 6806, pp. 680610–680611 (2008). https://doi.org/10.1117/12.766536
Palanisamy, G., Ponnusamy, P., Gopi, V.P.: An improved luminosity and contrast enhancement framework for feature preservation in color fundus images. Signal Image Video Process. 13(4), 719–726 (2019). https://doi.org/10.1007/s11760-018-1401-y
Soomro, T.A., Gao, J.: Non-invasive contrast normalisation and denosing technique for the retinal fundus image. Ann. Data Sci. 3(3), 265–279 (2016). https://doi.org/10.1007/s40745-016-0079-7
Soomro, T.A., Gao, J., Khan, M.A.U., Khan, T.M., Paul, M.: Role of image contrast enhancement technique for ophthalmologist as diagnostic tool for diabetic retinopathy. In: International Conference on Digital Image Computing: Technical Applications DICTA, pp. 1–8 (2016). https://doi.org/10.1109/DICTA.2016.7797078
Li, D., Zhang, L., Sun, C., Yin, T., Liu, C., Yang, J.: Robust retinal image enhancement via dual-tree complex wavelet transform and morphology-based method. IEEE Access 7, 47303–47316 (2019). https://doi.org/10.1109/ACCESS.2019.2909788
Li, D.M., Zhang, L.J., Yang, J.H., Su, W.: Research on wavelet-based contourlet transform algorithm for adaptive optics image denoising. Optik (Stuttg) 127(12), 5029–5034 (2016). https://doi.org/10.1016/j.ijleo.2016.02.042
Lidong, H., Wei, Z., Jun, W., Zebin, S.: Combination of contrast limited adaptive histogram equalisation and discrete wavelet transform for image enhancement. IET Image Process. 9(10), 908–915 (2015). https://doi.org/10.1049/iet-ipr.2015.0150
Walter, T., Klein, J.C.: Automatic analysis of color fundus photographs and its application to the diagnosis of diabetic retinopathy. In: Suri, J.S., Wilson, D.L., Laxminarayan, S. (eds.) Handbook of Biomedical Image Analysis. ITBE, pp. 315–368. Springer, Boston (2007). https://doi.org/10.1007/0-306-48606-7_7
Stefanou, H., Kakouros, S., Cavouras, D., Wallace, M.: Wavelet-based mammographic enhancement. In: 5th International Networking Conference INC, pp. 553–560 (2005)
Jia, C., Nie, S., Zhai, Y., Sun, Y.: Edge detection based on scale multiplication in wavelet domain. Jixie Gongcheng Xuebao/Chin. J. Mech. Eng. 42(1), 191–195 (2006). https://doi.org/10.3901/JME.2006.01.191
Mallat, S., Hwang, W.L.: Singularity detection and processing with wavelets. IEEE Trans. Info. Theory 38(2), 617–643 (1992). https://doi.org/10.1109/18.119727
Abdel Hamid, L.S., El-Rafei, A., El-Ramly, S., Michelson, G., Hornegger, J.: No-reference wavelet based retinal image quality assessment. In: 5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing VipIMAGE, pp. 123–130 (2016)
Nirmala, S.R., Dandapat, S., Bora, P.K.: Wavelet weighted distortion measure for retinal images. Signal Image Video Process. 7(5), 1005–1014 (2010). https://doi.org/10.1007/s11760-012-0290-8
Köhler, T., Budai, A., Kraus, M.F., Odstrčilik, J., Michelson, G., Hornegger, J.: Automatic no-reference quality assessment for retinal fundus images using vessel segmentation. In: Proceedings of the IEEE 26th Symposium Computer-Based Medical Systems CBMS, pp. 95–100 (2013). https://doi.org/10.1109/CBMS.2013.6627771
Dai, P., Sheng, H., Zhang, J., Li, L., Wu, J., Fan, M.: Retinal fundus image enhancement using the normalized convolution and noise removing. Int. J. Biomed. Imaging (2016). https://doi.org/10.1155/2016/5075612
Abdel-Hamid, L., El-Rafei, A., Michelson, G.: No-reference quality index for color retinal images. Comput. Biol. Med. 90, 68–75 (2017). https://doi.org/10.1016/j.compbiomed.2017.09.012
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ElMahmoudy, S., Abdel-Hamid, L., El-Rafei, A., El-Ramly, S. (2020). Wavelet-Based Retinal Image Enhancement. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12132. Springer, Cham. https://doi.org/10.1007/978-3-030-50516-5_27
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