UNRAVELLING DIABETIC RETINOPATHY THROUGH IMAGE PROCESSING, NEURAL NETWORKS AND FUZZY LOGIC â€“ A REVIEW
One of the main causes of blindness is diabetic retinopathy (DR) and it may affect people of any ages. In these days, both young and old ages are affectedÂ by diabetes, and the di abetes is the main cause of DR. Hence, it is necessary to have an automated system with good accuracy and less computationÂ time to diagnose and treat DR, and the automated system can simplify the work of ophthalmologists. The objective is to present an overview of variousÂ works recently in detecting and segmenting the various lesions of DR. Papers were categorized based on the diagnosing tools and the methods usedÂ for detecting early and advanced stage lesions. The early lesions of DR are microaneurysms, hemorrhages, exudates, and cotton wool spots and inÂ the advanced stage, new and fragile blood vessels can be grown. Results have been evaluated in terms of sensitivity, specificity, accuracy and receiverÂ operating characteristic curve. This paper analyzed the various steps and different algorithms used recently for the detection and classification of DRÂ lesions. A comparison of performances has been made in terms of sensitivity, specificity, area under the curve, and accuracy. Suggestions, future work
and the area to be improved were also discussed.
Keywords: Diabetic retinopathy, Image processing, Morphological operations, Neural network, Fuzzy logic.
2. Liu Q, Zou B, Chen J, Ke W, Yue K, Chen Z, et al. A location-to-segmentation strategy for automatic exudates segmentation in colour retinal fundus images. Comput Med Imaging Graph 2016;55:78-86.
3. Kumar HS, Bharathi PT, Madhuri R. A novel method for image analysis and exudates detection in retinal images. Int J Adv Res Innov 2016;4(1):219-23.
4. Besenczi R, TÃ³th J, Hajdu A. A review on automatic analysis techniques for color fundus photographs. Comput Struct Biotechnol J 2016;14:371-84.
5. Wu B, Zhu W, Shi F, Zhu S, Chen X. Automatic detection of microaneurysms in retinal fundus images. Comput Med Imaging Graph 2016;55:106-12.
6. Bharkad S. Automatic segmentation of optic disk in retinal images. Biomed Signal Process Control 2016;31:483-98.
7. Srivastava R, Duan L, Wong DW, Liu J, Wong TY. Detecting retinal microaneurysms and hemorrhages with robustness to the presence of blood vessels. Comput Methods Programs Biomed 2017;138:83-91.
8. Diyana M, Zulkifleya MA, Hussain A, Halim HW, Mustafa NB, Ting LS. Diabetic retinopathy assessment: Towards an automated system. Biomed Signal Process Control 2016;24:72-82.
9. Dhiravidachelvi E, Rajamani V. A novel approach for diagnosing diabetic retinopathy in fundus Images. J Comput Sci 2015;11(1):262-8.
10. Arenas-Cavalli JT, Rios SA, Pola M, Donoso R. A web-based platform for automated diabetic retinopathy screening. Procedia Comput Sci 2015;60:557-63.
11. Maher RS, Panchal D, Kayte J. Automatic diagnosis microaneurysm using fundus images. Int J Adv Res Comput Sci Softw Eng 2015;5(10):126-30.
12. Shriwas RS. Retinal image processing for diabetic retinopathy. Int J Eng Sci Res Technol 2015;594-598.ISSN:2277-9655.
13. Mustafa NB, Wan Zaki WM, Hussain A. A Review on the Diabetic Retinopathy Assessment Based on Retinal Vascular Tortuosity; 2015.
14. Mookiah MR, Acharya UR, Fujita H, Tan JH, Chua CK, Bhandary SV, et al. Application of different imaging modalities for diagnosis of Diabetic Macular Edema: A review. Comput Biol Med 2015;66:295-315.
15. Ganesan P, Chelladurai R, Sureshkumar M, Kalist V. Automatic identification and segmentation of exudates and optic disc in colour fundus images of the diabetic retinopathy human retina. Res J Pharm Biol Chem Sci 2015;6(4):908-15.
16. Banerjee S. Case based reasoning in the detection of retinal abnormalities using decision trees. Procedia Comput Sci 2015;46:402-8.
17. Bharali P, Medhi JP, Nirmala SR. Detection of Hemorrhages in Diabetic Retinopathy Analysis using Color Fundus Images; 2015.
18. Deka D, Medhi JP, Nirmala SR. Detection of Macula and Fovea for Disease Analysis in Color Fundus Images; 2015.
19. Prasad DK, Vibha L, Venugopal KR. Early Detection of Diabetic Retinopathy from Digital Retinal Fundus Images; 2015.
20. Omar M, Hossain A, Zhang L, Shum H. An Intelligent Mobile-based Automatic Diagnostic System to Identify Retinal Diseases using Mathematical Morphological Operations; 2014.
21. Yin F, Wong DW, Yow AP, Lee BH, Quan Y, Zhang Z, et al. Automatic retinal interest evaluation system (ARIES). Conf Proc IEEE Eng Med Biol Soc 2014;2014:162-5.
22. Usman Akram M, Khalid S, Tariq A, Khan SA, Azam F. Detection and classification of retinal lesions for grading of diabetic retinopathy. Comput Biol Med 2014;45:161-71.
23. Ramya J, Soundarya S, Nagoormeeral A, Revathi E. Detection of exudates in color fundus image. Int J Innov Res Sci Eng Technol 2014;3(3):10659-65.
24. Welikala RA, Dehmeshki J, Hoppe A, Tah V, Mann S, Williamson TH, et al. Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification. Comput Methods Programs Biomed 2014;114(3):247-61.
25. Tavakoli M, Shahri RP, Pourreza H, Mehdizadeh A, Banaee T,Toosi MH. A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy. Pattern Recognit 2013;46(10):2740-53.
26. Youssef D, Solouma NH. Accurate detection of blood vessels improves the detection of exudates in color fundus images. Comput Methods Programs Biomed 2012;108(3):1052-61.
27. Saleh MD, Eswaran C. An automated decision-support system for non-proliferative diabetic retinopathy disease based on MAs and HAs detection. Comput Methods Programs Biomed 2012;108(1):186-96.
28. Selvathi D, Prakash NB, Balagopal N. Automated detection of diabetic retinopathy for early diagnosis using feature extraction and support vector machine. Int J Emerg Technol Adv Eng 2012;2(11):103-8.
29. KÃ¶se C, Sevik U, Ikibas C, ErdÃ¶l H. Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images. Comput Methods Programs Biomed 2012;107(2):274-93.
30. Patil JD, Chaudhari AL. Tool for the detection of diabetic retinopathy using image enhancement method in DIP. Int J Appl Inf Syst IJAIS 2012;3(3):54-6.
31. Winder RJ, Morrow PJ, McRitchie IN, Bailie JR, Hart PM. Algorithms for digital image processing in diabetic retinopathy. Comput Med Imaging Graph 2009;33(8):608-22.
32. SÃ¡nchez CI, GarcÃa M, Mayo A, LÃ³pez MI, Hornero R. Retinal image analysis based on mixture models to detect hard exudates. Med Image Anal 2009;13(4):650-8.
33. Sopharak A, Uyyanonvara B, Barman S, Williamson TH. Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Comput Med Imaging Graph 2008;32(8):720-7.
34. Akila T, Kavitha G. Detection and classification of hard exudates in human retinal fundus images using clustering and random forest methods. Int J Emerg Technol Adv Eng 2014;4(2):24-9.
35. Rahim SS, Palade V, Shuttleworth J, Jayne C. Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing. Brain Inform 2016;3(4):249-67.
36. Pratt H, Coenen F, Broadbent DM, Harding SP, Zheng Y. Convolutional neural networks for diabetic retinopathy. Procedia Comput Sci 2016;90:200-5.
37. Mahendran G, Dhanasekaran R. Investigation of the severity level of diabetic retinopathy using supervised classifier algorithms. Comput Electr Eng 2015;45:312-23.
38. Divya SN. Detection of diabetic retinopathy using kirsch edge detection and watershed transformation algorithm. Int J Adv Res Ideas Innov Technol 2015;1(4):1-7.
39. Franklin SW, Rajan SE. Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images. Biocybern Biomed Eng 2014;34(2):117-24.
40. Thomas N, Mahesh TY. Detection and classification of exudates in diabetic retinopathy. Int J Adv Res Comput Sci Manag Stud 2014;2(9):296-305.
41. Akram MU, Khalid S, Khan SA. Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recognit 2013;46:107-16.
42. Basha AH, Udhayakumar S, Sujatha E. Detection of visual impairments using back propagation neural networks. Int J Comput Sci Eng Technol 2013;4(3):274-8.
43. Hassan SS, Bong DB, Premsenthil M. Detection of neovascularization in diabetic retinopathy. J Digit Imaging 2012;25(3):437-44.
44. Sanchez CI, Niemeijer M, Schulten MS, Abramoff M, van Ginneken B. Improving Hard Exudate Detection in Retinal Images Through a Combination of Local and Contextual Information; 2010.
45. GarcÃa M, LÃ³pez MI, Alvarez D, Hornero R. Assessment of four neural network based classifiers to automatically detect red lesions in retinal images. Med Eng Phys 2010;32(10):1085-93.
46. GarcÃa M, SÃ¡nchez CI, LÃ³pez MI, AbÃ¡solo D, Hornero R. Neural network based detection of hard exudates in retinal images. Comput Methods Programs Biomed 2009;93(1):9-19.
47. Basha SS, Prasad KS. Automatic detection of hard exudates in diabetic retinopathy using morphological segmentation and fuzzy logic. Int J Comput Sci Netw Secur 2008;8(12):211-8.
48. Dongare S, Rajendran S, Senthilkumari S, Gupta SK, Mathur R, Saxena R, et al. Genistein alleviates high glucose induced toxicity and angiogenesis in cultured human RPE cells. Int J Pharm Pharm Sci 2015;7(8):294-8.
49. Aly EM. FTIR analysis for retina associated with diabetic changes and treatment with oat. Int J Pharm Pharm Sci 2015;7(10):277-80.
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