The computer based method to diabetic retinopathy assessment in retinal images: a review
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Department of Medical Bioengineering, School of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz 51666, Iran
Department of Ophthalmology, Nikookari Eye Hospital, Tabriz University of Medical Sciences, Tabriz, Iran
Department of Ophthalmology, Faculty of Medicine University of Kermanshah, Kermanshah, Iran
School of Biomedical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK
Online publication date: 2019-04-17
Publication date: 2019-04-17
Electron J Gen Med 2019;16(2):em114
Diabetic retinopathy accounts for a considerable amount of the blindness especially among the patients who are between 20 to 60 years. The early detection of this disease plays an important role in preventing vision damages and appropriate follow-up care of diabetic eye. Manual investigation of color fundus images to check morphological changes in dark and bright lesions is tedious work and very time-consuming that can be made easily with the help of computer-aided diagnosis system. Many techniques were proposed for early detection of the abnormalities in the retinal images help the ophthalmologists recognize the retinopathy earlier. This paper presents a review of various automated algorithms that have been used for the detection of diabetic retinopathy.
Alghadyan AA. Diabetic retinopathy–an update. Saudi Journal of Ophthalmology. 2011;25(2):99-111. PMid:23960911 PMCid:PMC3729572.
Acharya UR, Faust O, Kadri NA, Suri JS, Yu W. Automated identification of normal and diabetes heart rate signals using nonlinear measures. Computers in biology and medicine. 2013;43(10):1523-9. PMid:24034744.
Fong DS, Aiello L, Gardner TW, King GL, Blankenship G, Cavallerano JD, et al. Retinopathy in diabetes. Diabetes care. 2004;27(suppl 1):s84-s7. PMid:14693935.
Nayak J, Acharya R, Bhat PS, Shetty N, Lim T-C. Automated diagnosis of glaucoma using digital fundus images. Journal of medical systems. 2009;33(5):337. PMid:19827259.
Ophthalmoscopy D, Levels E. International clinical diabetic retinopathy disease severity scale detailed table. 2002.
Crick RP, Khaw PT. A textbook of clinical ophthalmology: a practical guide to disorders of the eyes and their management: World Scientific Publishing Co Inc; 2003.
Foracchia M, Grisan E, Ruggeri A. Detection of optic disc in retinal images by means of a geometrical model of vessel structure. IEEE transactions on medical imaging. 2004;23(10):1189-95. PMid:15493687.
Youssif AA-HA-R, Ghalwash AZ, Ghoneim AASA-R. Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter. IEEE Transactions on Medical imaging. 2008;27(1):11-8. PMid:18270057.
Mendels F, Heneghan C, Harper P, Reilly R, Thiran J. Extraction of the optic disk boundary in digital fundus images. Proceedings BMES/EMBS 1999. 1999;2(EPFL-CONF-86620):1139.
Walter T, Klein J-C. Segmentation of color fundus images of the human retina: Detection of the optic disc and the vascular tree using morphological techniques. Medical data analysis. 2001:282-7.
Niemeijer M, Abràmoff MD, Van Ginneken B. Fast detection of the optic disc and fovea in color fundus photographs. Medical image analysis. 2009;13(6):859-70. PMid:19782633 PMCid:PMC2783621.
Reza AW, Eswaran C, Dimyati K. Diagnosis of diabetic retinopathy: automatic extraction of optic disc and exudates from retinal images using marker-controlled watershed transformation. Journal of medical systems. 2011;35(6):1491-501. PMid:20703768.
Sinthanayothin C, Boyce JF, Cook HL, Williamson TH. Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. British Journal of Ophthalmology. 1999;83(8):902-10. PMid:10413690.
Hoover A, Goldbaum M. Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE transactions on medical imaging. 2003;22(8):951-8. PMid:12906249.
Sedai S, Roy P, Mahapatra D, Garnavi R. Segmentation of Optic Disc and Optic Cup in Retinal Fundus Images Using Coupled Shape Regression. 2016.
Tjandrasa H, Wijayanti A, Suciati N. Optic nerve head segmentation using hough transform and active contours. Indonesian Journal of Electrical Engineering and Computer Science. 2012;10(3):531-6.
Li H, Chutatape O. Automated feature extraction in color retinal images by a model based approach. IEEE Transactions on biomedical engineering. 2004;51(2):246-54. PMid:14765697.
Esmaeili M, Rabbani H, Dehnavi AM. Automatic optic disk boundary extraction by the use of curvelet transform and deformable variational level set model. Pattern Recognition. 2012;45(7):2832-42.
Samanta S, Saha SK, Chanda B, editors. A simple and fast algorithm to detect the fovea region in fundus retinal image. Emerging Applications of Information Technology (EAIT), 2011 Second International Conference on; 2011: IEEE.
Paintamilselvi S. A novel method to detect the fovea of fundus retinal image. Int J Res Dev Eng(IJRDE). 2012;1(1).
Dehghani A, Moghaddam HA, Moin M-S. Optic disc localization in retinal images using histogram matching. EURASIP Journal on Image and Video Processing. 2012;2012(1):19.
Zhang D, Zhu W, Zhao H, Shi F, Chen X, editors. Automatic localization and segmentation of optical disk based on faster R-CNN and level set in fundus image. Medical Imaging 2018: Image Processing; 2018: International Society for Optics and Photonics.
Meng X, Xi X, Yang L, Zhang G, Yin Y, Chen X. Fast and Effective Optic disk localization based on Convolutional Neural Network. Neurocomputing. 2018.
Feman SS, Leonard-Martin TC, Andrews JS, Armbruster CC, Burdge TL, Debelak JD, et al. A quantitative system to evaluate diabetic retinopathy from fundus photographs. Investigative ophthalmology & visual science. 1995;36(1):174-81. PMid:7822145.
Sopharak A, Uyyanonvara B, Barman S. Automatic exudate detection from non-dilated diabetic retinopathy retinal images using fuzzy c-means clustering. Sensors. 2009;9(3):2148-61. PMid:22574005 PMCid:PMC3332251.
Sánchez CI, Hornero R, López MI, Aboy M, Poza J, Abásolo D. A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis. Medical Engineering & Physics. 2008;30(3):350-7. PMid:17556004.
Akram MU, Tariq A, Khan SA, Javed MY. Automated detection of exudates and macula for grading of diabetic macular edema. Computer methods and programs in biomedicine. 2014;114(2):141-52. PMid:24548898.
Partovi M, Rasta SH, Javadzadeh A. Automatic detection of retinal exudates in fundus images of diabetic retinopathy patients. 2016.
Sopharak A, Uyyanonvara B, Barman S, Williamson TH. Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Computerized medical imaging and graphics. 2008;32(8):720-7. PMid:18930631.
Esmaeili M, Rabbani H, Dehnavi A, Dehghani A. Automatic detection of exudates and optic disk in retinal images using curvelet transform. IET image processing. 2012;6(7):1005-13.
Fraz MM, Jahangir W, Zahid S, Hamayun MM, Barman SA. Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification. Biomedical Signal Processing and Control. 2017;35:50-62.
Gardner G, Keating D, Williamson TH, Elliott AT. Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. British journal of Ophthalmology. 1996;80(11):940-4. PMid:8976718.
Niemeijer M, van Ginneken B, Russell SR, Suttorp-Schulten MS, Abramoff MD. Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. Investigative ophthalmology & visual science. 2007;48(5):2260-7. PMid:17460289 PMCid:PMC2739583.
Sánchez CI, García M, Mayo A, López MI, Hornero R. Retinal image analysis based on mixture models to detect hard exudates. Medical Image Analysis. 2009;13(4):650-8. PMid:19539518.
Zhang X, Thibault G, Decencière E, Marcotegui B, Laÿ B, Danno R, et al. Exudate detection in color retinal images for mass screening of diabetic retinopathy. Medical image analysis. 2014;18(7):1026-43. PMid:24972380.
Rajan S, Das T, Krishnakumar R, editors. An Analytical Method for the Detection of Exudates in Retinal Images Using Invertible Orientation Scores. Proceedings of the World Congress on Engineering; 2016.
Liu Q, Zou B, Chen J, Ke W, Yue K, Chen Z, et al. A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images. Computerized Medical Imaging and Graphics. 2017;55:78-86. PMid:27665058.
Akyol K, Şen B, Bayır Ş, Cakmak HB. Assessing the importance of features for detection of hard exudates in retinal images. Turkish Journal of Electrical Engineering & Computer Sciences. 2017;25(2):1223-37.
Naqvi S, Zafar H, Ul HI. Automated System for Referral of Cotton-Wool Spots. Current diabetes reviews. 2016.
Pereira C, Gonçalves L, Ferreira M. Exudate segmentation in fundus images using an ant colony optimization approach. Information Sciences. 2015;296:14-24.
Spencer T, Olson JA, McHardy KC, Sharp PF, Forrester JV. An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus. Computers and biomedical research. 1996;29(4):284-302. PMid:8812075.
Frame AJ, Undrill PE, Cree MJ, Olson JA, McHardy KC, Sharp PF, et al. A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms. Computers in biology and medicine. 1998;28(3):225-38.
Rasta SH, Nikfarjam S, Javadzadeh A. Detection of retinal capillary nonperfusion in fundus fluorescein angiogram of diabetic retinopathy. BioImpacts: BI. 2015;5(4):183. PMid:26929922 PMCid:PMC4769788.
Fleming A, Goatman K, Williams G, Philip S, Sharp P, Olson J, editors. Automated detection of blot haemorrhages as a sign of referable diabetic retinopathy. Proc Medical Image Understanding and Analysis; 2008.
Streeter L, Cree MJ. Microaneurysm detection in colour fundus images. Image Vision Comput New Zealand. 2003:280-4.
Mane VM, Kawadiwale RB, Jadhav D, editors. Detection of Red lesions in diabetic retinopathy affected fundus images. Advance Computing Conference (IACC), 2015 IEEE International; 2015: IEEE.
Amiri SA, Hassanpour H, Shahiri M, Ghaderi R. Detection of microaneurysms in retinal angiography images using the circular Hough transform. J Adv Comput Res. 2008;3(1):1-12.
Shah SAA, Laude A, Faye I, Tang TB. Automated microaneurysm detection in diabetic retinopathy using curvelet transform. Journal of biomedical optics. 2016;21(10):101404-. PMid:26868326.
Soares I, Castelo-Branco M, Pinheiro AM. Microaneurysms detection using a novel neighborhood analysis. 2014.
Cervera MÁ, Paredes ME, Martínez RN, Ortiz CC, Hernández NR, editors. Development of a detection system microaneurysms in color fundus images. Electrical Engineering, Computing Science and Automatic Control (CCE), 2016 13th International Conference on; 2016: IEEE.
Seoud L, Hurtut T, Chelbi J, Cheriet F, Langlois JP. Red lesion detection using dynamic shape features for diabetic retinopathy screening. IEEE transactions on medical imaging. 2016;35(4):1116-26. PMid:26701180.
Esmaeili M, Rabbani H, Dehnavi AM, Dehghani A, editors. A new curvelet transform based method for extraction of red lesions in digital color retinal images. Image Processing (ICIP), 2010 17th IEEE International Conference on; 2010: IEEE.
Bharali P, Medhi JP, Nirmala S, editors. Detection of hemorrhages in diabetic retinopathy analysis using color fundus images. Recent Trends in Information Systems (ReTIS), 2015 IEEE 2nd International Conference on; 2015: IEEE.
Mumtaz R, Hussain M, Sarwar S, Khan K, Mumtaz S, Mumtaz M. Automatic detection of retinal hemorrhages by exploiting image processing techniques for screening retinal diseases in diabetic patients. International Journal of Diabetes in Developing Countries. 2017:1-8.
Staal J, Abràmoff MD, Niemeijer M, Viergever MA, Van Ginneken B. Ridge-based vessel segmentation in color images of the retina. IEEE transactions on medical imaging. 2004;23(4):501-9. PMid:15084075.
Zhou L, Rzeszotarski MS, Singerman LJ, Chokreff JM. The detection and quantification of retinopathy using digital angiograms. IEEE Transactions on Medical Imaging. 1994;13(4):619-26. PMid:18218540.
Nayak J, Bhat PS, Acharya R, Lim CM, Kagathi M. Automated identification of diabetic retinopathy stages using digital fundus images. Journal of medical systems. 2008;32(2):107-15. PMid:18461814.
Hoover A, Kouznetsova V, Goldbaum M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical imaging. 2000;19(3):203-10. PMid:10875704.
Akram MU, Jamal I, Tariq A, Imtiaz J, editors. Automated segmentation of blood vessels for detection of proliferative diabetic retinopathy. Biomedical and Health Informatics (BHI), 2012 IEEE-EMBS International Conference on; 2012: IEEE.
Esmaeili M, Rabbani H, Mehri A, Dehghani A, editors. Extraction of retinal blood vessels by curvelet transform. Image Processing (ICIP), 2009 16th IEEE International Conference on; 2009: IEEE.
Mann KS, Kaur S, editors. Segmentation of retinal blood vessels using artificial neural networks for early detection of diabetic retinopathy. AIP Conference Proceedings; 2017: AIP Publishing.
Jadhav A, Patil PB. Classification of diabetes retina images using Blood vessel area. International Journal on Cybernetics & Informatics (IJCI) Vol. 2015;4.
Zhu C, Zou B, Zhao R, Cui J, Duan X, Chen Z, et al. Retinal vessel segmentation in colour fundus images using Extreme Learning Machine. Computerized Medical Imaging and Graphics. 2017;55:68-77. PMid:27289537.
Hassan SSA, Bong DB, Premsenthil M. Detection of neovascularization in diabetic retinopathy. Journal of digital imaging. 2012;25(3):437-44. PMid:21901535 PMCid:PMC3348992.
Gupta G, Kulasekaran S, Ram K, Joshi N, Sivaprakasam M, Gandhi R. Local characterization of neovascularization and identification of proliferative diabetic retinopathy in retinal fundus images. Computerized Medical Imaging and Graphics. 2017;55:124-32. PMid:27634547.
Kar SS, Maity SP. Detection of neovascularization in retinal images using mutual information maximization. Computers & Electrical Engineering. 2017.
Huang H, Ma H, van Triest HJ, Wei Y, Qian W. Automatic detection of neovascularization in retinal images using extreme learning machine. Neurocomputing. 2017.
Akram MU, Khalid S, Tariq A, Javed MY. Detection of neovascularization in retinal images using multivariate m-Mediods based classifier. Computerized Medical Imaging and Graphics. 2013;37(5):346-57. PMid:23916066.
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