Hard Exudates Detection: A Review

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Emerging Technologies in Data Mining and Information Security

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1286))

Abstract

Hard exudates in the retina are the white or yellowish-white small deposits with sharp boundaries that appear as waxy, glistening or shiny surfaces. The hard exudates are located in the outer layer of retina and in deep to the retinal vessels. The damage on the retina of the eye, termed retinopathy may give us a clue to the vision injury or vision loss. Retinopathy is also evident in diabetic patients or in hypertension. This paper presents a complete review of some latest methods for detecting hard exudates in retinal images. This paper will help the researcher in studying the newest technique of various diabetic retinopathy and hypertensive retinopathy screening methodologies. The previously proposed techniques to detect hard exudates and retinal exudates are discussed in this present paper and the automated identification of hard exudates in hypertensive retinopathy is also discussed in the proposed paper.

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References

  1. Hard Exudates as a vascular disease. https://www.columbiaeye.org/education/digital-reference-of-ophthalmology/vitreous-retina/retinal-vascular-diseases/hard-exudates

  2. Niemeijer, M., van Ginneken, B., Russell, S.R., Suttorp-Schulten, M.S.A., Abràmoff, M.: Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. Invest. Ophthalmol. Vis. Sci. 48(5), 2260–2267 (2007). https://doi.org/10.1167/iovs.06-0996

  3. Kavitha, M., Palanib, S.: Hierarchical classifier for soft and hard exudates detection of retinal fundus images. J. Intell. Fuzzy Syst. 27, 2511–2528 (2014). https://doi.org/10.3233/ifs-141224 (IOS Press)

  4. Verma, S., Chandran, S.: Contactless palmprint verification system using 2-D Gabor filter and principal component analysis. Int. Arab J. Inf. Technol. 16(1) (2019)

    Google Scholar 

  5. Chandran, S., Verma, S.B.: Touchless palmprint verification using shock filter SIFT I-RANSAC and LPD IOSR. J. Comput. Eng. 17(3), 2278–8727 (2015)

    Google Scholar 

  6. Dhiravidachelvi, E., Rajamani, V., Janakiraman, P.A.: Identification of hard exudates in retinal images. Biomed. Res. (2017)

    Google Scholar 

  7. Klein, R., Klein, B.E., Moss, S.E., Davis, M.D., DeMets, D.L.: The Wisconsin epidemiologic study of diabetic retinopathy VII. Diabetic nonproliferative retinal lesions. Ophthalmology 94, 1389–1400 (1987)

    Article  Google Scholar 

  8. Long, S., Huang, X., Chen, Z., Pardhan, S., Zheng, D.: Automatic Detection of Hard Exudates in Colour Retinal Images Using Dynamic Threshold and SVM Classification: Algorithm Development and Evaluation. https://doi.org/10.1155/2019/3926930

  9. Database DIARETDB1—Standard Diabetic Retinopathy Database for retinal images. https://www.it.lut.fi/project/imageret/diaretdb1/index.html

  10. Saeed, E., Szymkowski, M., Saeed, K., Mariak, Z.: An approach to automatic hard exudate detection in retina color images by a telemedicine system based on the d-eye sensor and image processing algorithms

    Google Scholar 

  11. Marupally, A.G., Vupparaboina, K.K., Peguda, H.K., et al.: Semi-automated quantification of hard exudates in colour fundus photographs diagnosed with diabetic retinopathy. BMC Ophthalmol. 17, 172 (2017). https://doi.org/10.1186/s12886-017-0563-7

  12. Kaur, I., Kaur, N., Tanisha, Gurmeen, Deepi: Automated identification of hard exudates and cotton wool spots using biomedical image processing. Int. J. Comput. Sci. Technol. 7(4) (2016)

    Google Scholar 

  13. Narang, A., Narang, G., Singh, S.: Detection of hard exudates in colored retinal fundus images using the Support Vector Machine classifier. In: 2013 6th International Congress on Image and Signal Processing (CISP), Hangzhou, pp. 964–968 (2013). https://doi.org/10.1109/CISP.2013.6745304

  14. Benzamin, A., Chakraborty, C.: Detection of hard exudates in retinal fundus images using deep learning. In: 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Kitakyushu, Japan, pp. 465–469 (2018). https://doi.org/10.1109/iciev.2018.8641016

  15. Rokade, P., Manza, R.: Automatic detection of hard exudates in retinal images using Haar wavelet transform. Int. J. Appl. Innov. Eng. Manag. 4, 402–410 (2015). ISSN 2319-4847

    Google Scholar 

  16. Kavitha, D., Shenbaga Devi, S.: Automatic detection of optic disc and exudates in retinal images. In: Proceedings of 2005 International Conference on Intelligent Sensing and Information Processing, pp. 501–506 (2005)

    Google Scholar 

  17. Punnolil, A.: A novel approach for diagnosis and severity grading of diabetic maculopathy. In: 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1230–1235, 22–25 Aug 2013

    Google Scholar 

  18. Garcia, M.: Detection of hard exudates in retinal images using a radial basis function classifier. Ann. Biomed. Eng. 37(7), 1448–1463 (2009). https://doi.org/10.1007/s10439-009-9707-0

  19. Al Sariera, T.M., Rangarajan, L., Amarnath, R.: Detection and classification of hard exudates in retinal images. J. Intell. Fuzzy Syst. 38, 1943–1949 (2020). https://doi.org/10.3233/JIFS-190492 (IOS Press)

  20. Wang, H., Hsu, W., Goh, K.G., Lee, M.L.: An effective approach to detect lesions in color retinal images. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 181–186 (2000)

    Google Scholar 

  21. Ruba, T., Ramalakshmi, K.: Identification and segmentation of exudates using SVM classifier. In: IEEE International Conference on Innovations in Information Embedded and Communication Systems ICIIECS, pp. 1–6 (2015)

    Google Scholar 

  22. Sopharak, A., Uyyanonvarab, B., Barman, S., Williamson, T.H.: Automatic detection of diabetic retinopathy exudates from non- dilated retinal images using mathematical morphology methods. Comput. Med. Imaging Graph. 32, 720–727 (2008) (Elsevier)

    Google Scholar 

  23. Nugroho, H.A., Oktoeberza, K.Z.W., Adji, T.B., Bayu, S.M.: Segmentation of exudates based on high pass filtering in retinal fundus images. In: ICITEE IEEE, pp. 436–441 (2015)

    Google Scholar 

  24. Manoj Kumar, S.B., Manjunath, R., Sheshadri, H.S.: Feature extraction from the fundus images for the diagnosis of diabetic retinopathy. In: Emerging Research in Electronics, Computer Science, pp. 240–245. IEEE (2015)

    Google Scholar 

  25. Harangi, B., Hajdu, A.: Automatic exudate detection by fusing multiple active contours and regionwise classification. Comput. Biol. Med. 54, 156–171 (2014)

    Article  Google Scholar 

  26. Wisaeng, K., Sa-Ngiamviboo, W.: Exudates detection using morphology mean shift algorithm in retinal images. IEEE Access 7, 11946–11958 (2019). https://doi.org/10.1109/ACCESS.2018.2890426

    Article  Google Scholar 

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Correspondence to Abhay Kumar Yadav .

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Verma, S.B., Yadav, A.K. (2021). Hard Exudates Detection: A Review. In: Hassanien, A.E., Bhattacharyya, S., Chakrabati, S., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1286. Springer, Singapore. https://doi.org/10.1007/978-981-15-9927-9_12

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