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Survey of Automatic Detection of Diabetic Retinopathy using digital image processing

Saurabh. S. Athalye1 , Gaurav Vijay2

Section:Survey Paper, Product Type: Journal Paper
Volume-7 , Issue-3 , Page no. 352-355, Mar-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i3.352355

Online published on Mar 31, 2019

Copyright © Saurabh. S. Athalye, Gaurav Vijay . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Saurabh. S. Athalye, Gaurav Vijay, “Survey of Automatic Detection of Diabetic Retinopathy using digital image processing,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.352-355, 2019.

MLA Style Citation: Saurabh. S. Athalye, Gaurav Vijay "Survey of Automatic Detection of Diabetic Retinopathy using digital image processing." International Journal of Computer Sciences and Engineering 7.3 (2019): 352-355.

APA Style Citation: Saurabh. S. Athalye, Gaurav Vijay, (2019). Survey of Automatic Detection of Diabetic Retinopathy using digital image processing. International Journal of Computer Sciences and Engineering, 7(3), 352-355.

BibTex Style Citation:
@article{Athalye_2019,
author = {Saurabh. S. Athalye, Gaurav Vijay},
title = {Survey of Automatic Detection of Diabetic Retinopathy using digital image processing},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2019},
volume = {7},
Issue = {3},
month = {3},
year = {2019},
issn = {2347-2693},
pages = {352-355},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3844},
doi = {https://doi.org/10.26438/ijcse/v7i3.352355}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i3.352355}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3844
TI - Survey of Automatic Detection of Diabetic Retinopathy using digital image processing
T2 - International Journal of Computer Sciences and Engineering
AU - Saurabh. S. Athalye, Gaurav Vijay
PY - 2019
DA - 2019/03/31
PB - IJCSE, Indore, INDIA
SP - 352-355
IS - 3
VL - 7
SN - 2347-2693
ER -

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Abstract

Diabetic Retinopathy is brutal eye disease, which is acting as a major cause of blindness in young or middle age population. In this disease there are major chances of losing vision by patient. According to many eye specialists, it is tough to detect this disease in its early stage. If we could able to detect this disease in early stage we can save patient’s vision. For this purpose doctors recommend periodical checking of eyes by specialist. But in country like India, number of specialists available is not at all sufficient for the overall population of the country. It is also a fact that, these specialists are mostly available for city population. In rural areas there is scarcity of eye specialists and testing equipment’s. In this scenario periodical screening programs and automated Diabetic Retinopathy detection can help a lot. Numbers of researchers are attracted towards research on Automatic DR detection. Proposed paper focuses on medical background of DR and comparison of some existing methods for automatic DR detection.

Key-Words / Index Term

Diabetic Retinopathy (DR), exudates (EXs), microaneurysms (MAs), hemorrhages (HMs)

References

[1] Fraz, M.M., Jahangir, W., Zahid, S., Hamayun, M.M. and Barman, S.A., “Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification,” Biomedical Signal Processing and Control, vol. 35, pp.50-62, 2017.
[2] Zhu, C., Zou, B., Zhao, R., Cui, J., Duan, X., Chen, Z. and Liang, Y., “Retinal vessel segmentation in colour fundus images using Extreme Learning Machine,” Computerized Medical Imaging and Graphics, vol. 55, pp.68-77, 2017.
[3] W. Zhou, C. Wu, D. Chen, Y. Yi and W. Du, "Automatic Microaneurysm Detection Using the Sparse Principal Component Analysis-Based Unsupervised Classification Method," in IEEE Access, vol. 5, pp. 2563-2572, 2017.
[4] Amin, J., Sharif, M., Yasmin, M., Ali, H. and Fernandes, S.L., “A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions,” Journal of Computational Science, vol. 19, pp.153-164, 2017.
[5] Leontidis, G., “A new unified framework for the early detection of the progression to diabetic retinopathy from fundus images,” Computers in Biology and Medicine, 2017.
[6] L. Ngo and J. H. Han, "Multi-level deep neural network for efficient segmentation of blood vessels in fundus images," in Electronics Letters, vol. 53, no. 16, pp. 1096-1098, 8 3 2017.
[7] Abbas, Q., Fondon, I., Sarmiento, A., Jiménez, S. and Alemany, P., “Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features,” Medical & Biological Engineering & Computing, pp.1-16, 2017.
[8] W. Zhou, C. Wu, Y. Yi and W. Du, "Automatic Detection of Exudates in Digital Color Fundus Images Using Superpixel Multi-Feature Classification," in IEEE Access, vol. 5, no. , pp. 17077-17088, 2017.
[9] Mane, V.M. and Jadhav, D.V., “Holoentropy enabled-decision tree for automatic classification of diabetic retinopathy using retinal fundus images,” Biomedical Engineering/Biomedizinische Technik, vol. 62, no. 3, pp.321-332, 2017.
[10] L. Seoud, T. Hurtut, J. Chelbi, F. Cheriet and J. M. P. Langlois, "Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening," in IEEE Transactions on Medical Imaging, vol. 35, no. 4, pp. 1116-1126, April 2016.
[11] S. W. Franklin and S. E. Rajan, "Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images," in IET Image Processing, vol. 8, no. 10, pp. 601-609, Oct. 2014.
[12] Hari, V.S., Raj, V.J. and Gopikakumari, R., “Quadratic filter for the enhancement of edges in retinal images for the efficient detection and localization of diabetic retinopathy,” Pattern Analysis and Applications, vol. 20, no. 1, pp.145-165, 2017.
[13] S. Roychowdhury, D. D. Koozekanani, S. N. Kuchinka and K. K. Parhi, "Optic Disc Boundary and Vessel Origin Segmentation of Fundus Images," in IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 6, pp. 1562-1574, Nov. 2016.
[14] Kumar, P.S., Deepak, R.U., Sathar, A., Sahasranamam, V. and Kumar, R.R., “Automated Detection System for Diabetic Retinopathy Using Two Field Fundus Photography” Procedia Computer Science, vol. 93, pp.486-494, 2016.
[15] Rahim, S.S., Palade, V., Shuttleworth, J. and Jayne, C., “Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing,” Brain informatics, vol. 3, no. 4, pp.249-267, 2016.
[16]http://www.eagleeyecentre.com.sg/service/diabetic-retinopathy/
[17] https://www.bondeye.com/part-2-diabetes-affect-eyes-roger-t-adler-md/diabetic-retinopathy-2