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Computerised approaches for the detection of diabetic retinopathy using retinal fundus images: a survey

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Abstract

Eye-related disease such as diabetic retinopathy (DR) is a medical ailment in which the retina of the human eye is smashed because of damage to the tiny retinal blood vessels in the retina. Ophthalmologists identify DR based on various features such as the blood vessels, textures and pathologies. With the rapid development of methods of analysis of biomedical images and advanced computing techniques, image processing-based software for the detection of eye disease has been widely used as an important tool by ophthalmologists. In particular, computer vision-based methods are growing rapidly in the field of medical images analysis and are appropriate to advance ophthalmology. These tools depend entirely on visual analysis to identify abnormalities in Retinal Fundus images. During the past two decades, exciting improvement in the development of DR detection computerised systems has been observed. This paper reviews the development of analysing retinal images for the detection of DR in three aspects: automatic algorithms (classification or pixel to pixel methods), detection methods of pathologies from retinal fundus images, and extraction of blood vessels of retinal fundus image algorithms for the detection of DR. The paper presents a detailed explanation of each problem with respect to retinal images. The current techniques that are used to analyse retinal images and DR detection issues are also discussed in detail and recommendations are made for some future directions.

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References

  1. Faust O et al (2010) Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review. J Med Syst 36(1):145–157

    Article  Google Scholar 

  2. Niemeijer M et al (2010) Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs. IEEE Trans Med Imaging 29(1):185–195

    Article  Google Scholar 

  3. Malaysia PD (2009) Clinical practice guidelines (CPG) management of type 2 diabetes mellitus. Min Health Malays Malays Endocr Metabolic Soc Acad Med Malays 1:1000

    Google Scholar 

  4. Hani A, Soomro TA (2013) Non-invasive contrast enhancement for retinal fundus imaging. IEEE Int Conf Control Syst Comput Eng (ICCSCE) 1:197–202

    Google Scholar 

  5. Soomro TA (2014) Non-invasive image denoising and contrast enhancement techniques for retinal fundus images. Master Thesis. Electrical and Electronic Engineering Department Universiti Teknologi Petronas

  6. Sinthanayothin C, Boyce J (2002) Automated detection of diabetic retinopathy on digital fundus images. Diabet Med 19(2):105–112

    Article  Google Scholar 

  7. Niemeijer M, van Ginneken B (2005) Automatic detection of red lesions in digital color fundus photographs. IEEE Trans Med Imaging 24(5):584–592

    Article  Google Scholar 

  8. Matsopoulos G, Mouravliansky N, Delibasis KK, Nikita KS (1999) Automatic retinal image registration scheme using global optimization techniques. IEEE Trans Inf Technol Biomed 3(1):47–60

    Article  Google Scholar 

  9. Wu T (1993) Review of diabetes: Identification of markers for early detection, glycemic control, and monitoring clinical complications. J Clin Lab Anal 7:293–300

    Article  Google Scholar 

  10. Huan W, Hsu W, Guan GK, Li LM (2000) An effective approach to detect lesions in color retinal images. IEEE Conf Comput Vis Pattern Recognit 2:181–186

    Google Scholar 

  11. Collins N (2003) Diabetic retinopathy preferred practice pattern. American Academy of opthhalmology, San Francisco

    Google Scholar 

  12. Herbert M, Jelinek F (2010) Automated Image Detection of Retinal Pathology, 1st edn. CRC Press, Boca Raton

    Google Scholar 

  13. Fujita H, Uchiyama Y, Nakagawa T, Fukuoka D, Hatanaka Y, Hara T, Lee GN, Hayashi Y, Ikedo Y, Gao X, Zhou X (2008) Computer-aided diagnosis: the emerging of three CAD systems induced by Japanese health care needs. Comput Methods Programs Biomed. 92(3):238–248

    Article  Google Scholar 

  14. Lee SC, Lee ET, Kingsley RM, Wang Y, Russell D, Klein R, Warn A (2001) Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts. Arch Ophthalmol 119(4):509–526

    Article  Google Scholar 

  15. Wang H, Hsu W, Goh KG, Lee ML (2000) An effective approach to detect lesions in color retinal images. IEEE Comput Soc Conf Comput Vis Pattern Recognit (CVPR) 2:181–186

    Google Scholar 

  16. Sinthanayothin C, Kongbunkiat V, Phoojaruenchanachai S, Singalavanija A (2003) Automated screening system for diabetic retinopathy. In: The 3rd international symposium on image and signal processing and analysis. vol 2, pp 915–920

  17. Lee SC, Lee ET, Wang Y, Klein R, Kingsley RM, Warn A (2005) Computer classification of a nonproliferative diabetic retinopathy. Arch Ophthalmol 123(6):759–764

    Article  Google Scholar 

  18. Singalavanija Supokavej A, Bamroongsuk J, Sinthanayothin P (2006) Feasibility study on computer-aided screening for diabetic retinopathy. J Ophthalmol 50:361–366

    Google Scholar 

  19. Kahai P, Namuduri KR, Thompson H (2006) A decision support framework for automated screening of diabetic retinopathy. Int J Biomed Imag 2:1630–1634

    Google Scholar 

  20. Wong Acharya LY, Venkatesh UR, Chee YV, Lim C (2008) Identification of different stages of diabetic retinopathy using retinal optical images. J Inform Sci 178(1):106–121

    Article  Google Scholar 

  21. Nayak J, Bhat PS, Acharya R, Lim CM, Kagathi M (2008) Automated identification of different stages of diabetic retinopathy using digital fundus images. J Med Syst 32(2):107–115

    Article  Google Scholar 

  22. Acharya R, Tan PH, Subramaniam T, Tamura T, Chua KC, Goh SC, Lim CM, Goh SY, Chung KR, Law C (2008) Automated identification of diabetic type 2 subjects with and without neuropathy using wavelet transform on pedobarograph. J Med Syst 32(1):21–29

    Article  Google Scholar 

  23. Larsen N, Godt J, Grunkin M, Lund-Andersen H, Larsen M (2003) Automated detection of diabetic retinopathy in a fundus photographic screening population. Invest Ophthalmol Vis Sci 44(2):767–771

    Article  Google Scholar 

  24. Usher D, Dumsky M (2003) Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet Med 21:84–90

    Article  Google Scholar 

  25. Neubauer Chryssafis (2005) Screening for diabetic retinopathy and optic disc topography with the retinal thickness analyzer. Ophthalmologe. 102(3):251–259

    Article  Google Scholar 

  26. Lee SC, Lee ET, Wang Y, Klein R, Kingsley RM, Warn A (2005) Computer classification of nonproliferative diabetic retinopathy. Arch Ophthalmol 123(6):759–764

    Article  Google Scholar 

  27. Phillips R, Forrester J (1993) Automated detection and quantification of retinal exudates. Graefes Arch Clin Exp Ophthalmol 231(2):90–94

    Article  Google Scholar 

  28. de Estabridis Figueiredo (2007) Automatic detection and diagnosis of diabetic retinopathy. IEEE Int Conf Image Process ICIP 2:445–448

    Google Scholar 

  29. Li Jin (2008) Screening diabetic retinopathy through color retinal images. Proc 1st Int Conf Med Biom 1:176–183

    Google Scholar 

  30. Abrmoff MN, Niemeijer M, Suttorp-Schulten MS, Viergever MA, Russell SR, Van Ginneken B (2008) Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes. Diabetes Care 31(2):193–205

    Article  Google Scholar 

  31. Vujosevic S, Benetti E, Massignan F, Pilotto E, Varano M, Cavarzeran F, Avogaro A, Midena E (2009) Screening for diabetic retinopathy: 1 and 3 nonmydriatic 45-degree digital fundus photographs vs 7 standard early treatment diabetic retinopathy study fields. Am J Ophthalmol 148(1):111–119

    Article  Google Scholar 

  32. Abramoff MD, Reinhardt JM, Russell SR, Folk JC, Mahajan VB, Niemeijer M et al (2010) Automated early detection of diabetic retinopathy. Ophthalmology 117(1147–1154):6

    Google Scholar 

  33. Agurto C, Barriga ES, Murray V, Nemeth S, Crammer R, Bauman W et al (2011) Automatic detection of diabetic retinopathy and age-related macular degeneration in digital fundus images. Invest Ophthalmol Vis Sci 52(8):5862–5871

    Article  Google Scholar 

  34. Hassan SSA, Bong DBL, Premsenthil M (2012) Detection of neovascularization in diabetic retinopathy. J Digit Imaging 25:437–444

    Article  Google Scholar 

  35. Rahim SS, Palade V, Shuttleworth J, Jayne C (2016) Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing. Brain Inform 3:249–267

    Article  Google Scholar 

  36. Kahai P, Namuduri K, Thompson H (2004) Decision support for automated screening of diabetic retinopathy. The thirty-eighth asilomar conference on signals. Syst Comput 1:1–7

    Google Scholar 

  37. Soto-Pedre E, Navea A, Millan S, Hernaez-Ortega MC, Morales J, Desco MC et al (2015) Evaluation of automated image analysis software for the detection of diabetic retinopathy to reduce the ophthalmologists workload. Acta Ophthalmol 93(1):52–56

    Article  Google Scholar 

  38. Baudoin JCK, Klein JC (1996) Automatic detection of microaneurysms in diabetic fluorescein angiography. Comput Biomed Res 32(3):254–261

    Google Scholar 

  39. Spencer T, Olson JA, Mchardy K, Sharp PF, Forrester JV (1996) An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus. Comput Biomed Res 29(4):284–302

    Article  Google Scholar 

  40. Walter T, Klein JC (2002) A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina. IEEE Trans Med Imaging 10:1236–43

    Article  Google Scholar 

  41. Gardner G, Keating, Gardner GG, Keating D, Williamson TH, Elliott AT (1996) Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Br J Ophthalmol 80(11):940–947

    Article  Google Scholar 

  42. Luo G, Chutatape O, Li H, Krishnan SM (2001) Abnormality detection in automated mass screening system of diabetic retinopathy. In: Proceedings of the IEEE symposium on computer-based medical systems (CBMS). vol 1, pp. 132–137

  43. Grisan AE, Ruggeri A (2007) Segmentation of candidate dark lesions in fundus images based on local thresholding and pixel density. In: The 29th annual international conference of the IEEE engineering in medicine and biology society (EMBS), vol 1, pp 6736–6745

  44. Garcia M, Sanchez CI, Lopez MI, Diez A, Hornero R (2008) Automatic detection of red lesions in retinal images using a multilayer perceptron neural network. In: The annual international conference of the IEEE engineering in medicine and biology society (EMBS), vol 10, pp 5425–5433

  45. Zhang X, Chutatape KK (2005) A SVM approach for detection of hemorrhages in background diabetic retinopathy. IEEE Int Joint Conf Neural Netw 4:2435–2440

    Google Scholar 

  46. Zhang X, Chutatape O (2005) Top-down and bottom-up strategies in lesion detection of background diabetic retinopathy. IEEE Comput Soc Conf Comput Vis Pattern Recognit 2:422–428

    Google Scholar 

  47. Quellec G, Lamard M (2008) Optimal wavelet transform for the detection of microaneurysms in retina photographs. IEEE Trans Med Imaging 27(9):1230–1248

    Article  Google Scholar 

  48. Quellec G, Lamard M (2006) Detection of lesions in retina photographs based on the wavelet transform. In: The 28th annual international conference of the IEEE engineering in medicine and biology society (EMBS), vol 1, pp 2618–2621

  49. Cree J, Olsoni JA, McHardyt KC, Forresters JV, Sharp PF (1996) Automated microaneurysm detection. Int Conf Image Process 1:700–702

    Google Scholar 

  50. Frame, Undrilla PE, Creea MJ, Olsonb JA, McHardyc KC, Sharpa PF et al (1998) A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms. Comput Biol Med 28(3):225–238

    Article  Google Scholar 

  51. Ege B, Hejlesena OK, Larsena OV, Møllera K, Jenningsb B, Kerrb D et al (2000) Screening for diabetic retinopathy using computer based image analysis and statistical classification. Comput Methods Programs Biomed 62(3):165–175

    Article  Google Scholar 

  52. Hipwell JH, Strachan F, Olson JA, McHardy KC, Sharp PF, Forrester JV (2000) Automated detection of microaneurysms in digital red-free photographs: a diabetic retinopathy screening tool. Diabetic Med 17(8):588–594

    Article  Google Scholar 

  53. Yang G, Gagnonz L, Wangy S, Boucher MC (2001) Algorithm for detecting micro-aneurysms in low-resolution color retinal images. In: Proceedings of vision interfaces (VI) vol 4, pp 265–271

  54. Walter T, Klein JC (2002) Automatic detection of microaneurysms in color fundus images of the human retina by means of the bounding box closing. In: Medical Data Analysis, vol 2526, pp 210–220

  55. Walter T, Klein JC (2002) Automatic detection of microaneurysms in color fundus images of the human retina by means of the bounding box closing. Springer Med Data Anal 2525:220–230

    MATH  Google Scholar 

  56. Pallawala P, Hsu W, Lee ML, Goh SS (2005) Automated microaneurysm segmentation and detection using generalized eigenvectors. In: Seventh IEEE workshops on application of computer vision (WACV/MOTION), vol 1, pp 322–327

  57. Fleming Philip S, Goatman KA, Olson JA (2006) Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Trans Med Imaging 25(9):1223–1232

    Article  Google Scholar 

  58. Bhalerao A, Patanaik A, Anand S, Saravanan P (2008) Robust detection of microaneurysms for sight threatening retinopathy screening. In: The 2008 sixth Indian conference on computer vision, graphics and image processing. vol 1, pp 520–527

  59. Kande GB, Savithri TS, Subbaiah PV (2010) Automatic detection of microaneurysms and hemorrhages in digital fundus images. J Digit Imaging 23(4):430–437

    Article  Google Scholar 

  60. Zhang B, Wu X, You J, Li Q, Karray F (2010) Detection of microaneurysms using multi-scale correlation coefficients. Pattern Recognit 43(6):2237–2248

    Article  Google Scholar 

  61. Sopharak A, Uyyanonvara B, Barman S, Williamson T (2011) Automatic microaneurysm detection from non-dilated diabetic retinopathy retinal images. In: Proceedings of the world congress on engineering WCE London, vol 2, pp 1–4

  62. Gowthaman R (2014) Automatic identification and classification of microaneurysms for detection of diabetic retinopathy. Int J Res Eng Technol 3(2):464–473

    Article  Google Scholar 

  63. Maher RS, Ambedkar B, Kayte SN, Ambedkar B, Dhopeshwarkar M, Ambedkar B (2015) Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs for screening population. Int J Comput Appl 131(3):0975–8887

    Google Scholar 

  64. Sehirli E, Turan MK, Dietzel A (2015) Automatic detection of microaneurysms in rgb retinal fundus images. Int J Sci Technol Res 1(8):1–7

    Google Scholar 

  65. Hatanaka Y, Nakagawa T, Hayashi Y, Hara T, Fujita H (2008) Improvement of automated detection method of hemorrhages in fundus images. In: 30th annual international conference of the IEEE engineering in medicine and biology society (EMBS), vol 1, pp 5429–5432

  66. Kleawsirikul N, Gulati S, Uyyanonvara B (2013) Automated retinal hemorrhage detection using morphological top hat and rule-based classification. Int Conf Intell Comput Syst (ICICS) 1:39–43

    Google Scholar 

  67. Sahu D, Meshram S (2016) Automatic detection of hemorrhages using image processing technique. Int J Eng Sci Res Technol 5(6):853–857

    Google Scholar 

  68. Junior SB, Welfer D (2013) Automatic detection of microaneurysms and hemorrhages in color eye fundus images. Int J Comput Sci Inf Technol (IJCSIT) 5(5):21–37

    Google Scholar 

  69. Mane VM, Jadhav DV, Bansod A (2015) An automatic approach to Hemorrhages detection. Int Conf Inf Process (ICIP) 1:135–138

    Google Scholar 

  70. Zheng L, Chutatape (1997) Automatic image analysis of fundus photograph. In: 19th annual international conference of the IEEE engineering in medicine and biology society. vol 2, pp 524–525

  71. Goldbaum MH, Katz NP, Nelson MR, Haff LR (1990) The discrimination of similarly colored objects in computer images of the ocular fundus. Investig Ophthalmol Visual Sci 31(4):617–623

    Google Scholar 

  72. Cote B, Goldbaum M, Chaudhuri S, Chatterjee S, Nelson M (1991) Robust-detection, and precise localization of the optic-nerve in digital ocular fundus images. Investig Ophthalmol Visual Sci 32(4):691–691

    Google Scholar 

  73. Sanchez CI, García M, Mayo A, López MI, Hornero R (2009) Retinal image analysis based on mixture models to detect hard exudates. Med Image Anal 13(4):650–658

    Article  Google Scholar 

  74. Osareh A (2004) Automated identification of diabetic retinal exudates and the optic disc. PhD thesis, Department of Computer Sciene, University of Bristol. 1:300

  75. Sinthanayothin C, Boyce JF, Williamson TH, Cook HK, Mensah E, S Lal DU (2002) Automated detection of diabetic retinopathy on digital fundus images. Diabet Med 19(2):105–112

    Article  Google Scholar 

  76. Niemeijer M, van Ginneken B, Russell SR, Suttorp-Schulten MS, Abramoff MD (2007) Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. Investig Ophthalmol Visual Sci 48(5):2260–2267

    Article  Google Scholar 

  77. Osareh A, Shadgar B, Markham R (2009) A computational-intelligencebased approach for detection of exudates in diabetic retinopathy images. IEEE Trans Inf Technol Biomed 13(4):535–545

    Article  Google Scholar 

  78. Ram K, Sivaswamy J (2009) Multi-space clustering for segmentation of exudates in retinal color photographs. In: Annual international conference of the IEEE engineering in medicine and biology society (EMBS), vol 1, pp 1437–1440

  79. Ravishankar Jain A, Mittal A (2009) Automated feature extraction for early detection of diabetic retinopathy in fundus images. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition (CVPR), vol 1, pp 210–217

  80. Xu L, Luo S (2009) Support vector machine based method for identifying hard exudates in retinal images. In Proceedings of the IEEE youth conference on information, computing and telecommunication (YC-ICT), vol 1, pp 138–141

  81. Welfer D, Scharcanski J, Marinho DR (2010) A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images. Comput Med Imaging Graph 34:228–235

    Article  Google Scholar 

  82. Eadgahi MGF, Pourreza H (2012) Localization of hard exudates in retinal fundus image by mathematical morphology operations. In: 2nd international eConference on computer and knowledge engineering (ICCKE), vol 1, pp 185–189

  83. Zhanga X, Thibaulta G, Decencierea E, Marcoteguia B, Layd B, Dannod R et al (2014) Exudate detection in color retinal images for mass screening of diabetic retinopathy. Med Image Anal 18(7):1026–1043

    Article  Google Scholar 

  84. Omar M, Hossain A, Zhang L, Shum H (2014) An intelligent mobile-based automatic diagnostic system to identify retinal diseases using mathematical morphological operations. In: 8th international conference on software, knowledge, information management and applications (SKIMA), vol 1, pp 1–5

  85. Haloi M, Dandapat S, Sinha R (2015) A Gaussian scale space approach for exudates detection, classification and severity prediction. ArXiv. 2015. pp 1–7

  86. Alharthi ASA, Emamian V (2016) An Automated mechanism for early screening and diagnosis of diabetic retinopathy in human retinal images. Br J Appl Sci Technol 12(1):1–15

    Article  Google Scholar 

  87. Pakter HM, Ferlin E, Fuchs SC, Maestri MK, Moraes RS, Nunes G et al (2005) Measuring arteriolar-to-venous ratio in retinal photography of patients with hypertension: development and application of a new semi-automated method. Am J Hypertens 18:417–421

    Article  Google Scholar 

  88. Wong TY, Knudtson MD, Klein R, Klein BEK, Meuer MSM, Hubbard LD (2004) Computer-assisted measurement of retinal vessel diameters in the Beaver Dam Eye Study: methodology, correlation between eyes, and effect of refractive errors. J Ophthalmol 111:1181–1190

    Google Scholar 

  89. Kirbas C, Quek F (2004) A review of vessel extraction techniques and algorithms. ACM Comput Surv 36:81–121

    Article  Google Scholar 

  90. Akita K, Kuga H (1982) A computer method of understanding ocular fundus images. Pattern Recognit 5(16):431–443

    Article  Google Scholar 

  91. Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M (1989) Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans Med Imaging 8(3):263–269

    Article  Google Scholar 

  92. Nekovei R, Sun Y (1995) Back-propagation network and its configuration for blood vessel detection in angiograms. IEEE Trans Neural Netw 6:64–72

    Article  Google Scholar 

  93. Tolias YA, Panas SM (1998) A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Trans Med Imaging 17:263–273

    Article  Google Scholar 

  94. Kochner B, Schuhmann D, Michaelis M, Mann G, Englmeier KH (1998) Course tracking and contour extraction of retinal vessels from colour fundus photographs: most effcient use of steerable filters for model based image analysis. In: SPIE 3338, medical imaging, vol 1, pp 1–7

  95. Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. Medical image computing and computer-assisted intervention MICCAITM98 Springer, Berlin vol 1496, 130–137

  96. Sinthanayothin Boyce C, Cook JF, Williamson HL (1999) Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. Br J Ophthalmol 83(8):902–910

    Article  Google Scholar 

  97. Martinez-Perez M, Hughes A, Stanton A, Thom S, Bharath A, Parker K (1999) Retinal blood vessel segmentation by means of scale-space analysis and region growing. In: The second international conference on medical image computing and computer-assisted, vol 1, pp 90–97

  98. Hoover KA (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched flter response. IEEE Trans Med Imaging 19(3):203–210

    Article  Google Scholar 

  99. Simo A, de Ves E (2001) Segmentation of macular fluorescein angiographies. A statistical approach. Pattern Recognit 34:795–809

    Article  MATH  Google Scholar 

  100. Zana F, Klein JC (2001) Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans Image Process 10:1010–1019

    Article  MATH  Google Scholar 

  101. Gang L, Chutatape O, Krishnan SM (2002) Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter. IEEE Trans Biomed Eng 49:168–172

    Article  Google Scholar 

  102. Jiang X, Mojon D (2003) Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Trans Pattern Anal Mach Intell 25:131–137

    Article  Google Scholar 

  103. Niemeijer M, Staal J, van Ginneken B, Loog M, Abramoff M (2004) Comparative study on retinal vessel segmentation methods on a new publicly available database. SPIE Medical Imaging 5370:648–656

    Google Scholar 

  104. Staal J, Abramof MD, Niemeijer M, Viergever MA, van Ginneken B (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23:501–509

    Article  Google Scholar 

  105. Wink O, Niessen WJ, Viergever MA (2004) Multiscale vessel tracking. IEEE Trans Med Imaging 23:130–133

    Article  Google Scholar 

  106. Vermeer KA, Vos FM, Lemij H, Vossepoel AM (2004) A model based method for retinal blood vessel detection. Comput Biol Med 34:209–219

    Article  Google Scholar 

  107. Mahadevan V, Narasimha-Iyer H, Roysam B, Tanenbaum HL (2004) Robust model-based vasculature detection in noisy biomedical images. IEEE Trans Inf Technol Biomed 8:360–376

    Article  Google Scholar 

  108. Ayala G, Leon T, Zapater V (2005) Different averages of a fuzzy set with an application to vessel segmentation. IEEE Trans Fuzzy Syst 1(3):384–393

    Article  Google Scholar 

  109. Soares JVB, Roberto JJGL, Cesar M, Jelinek JHF, Cree MJ (2006) Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans Med Imaging 25(9):1214–1222

    Article  Google Scholar 

  110. Mendonca AM, Campilho A (2006) Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans Med Imaging 25:1200–1213

    Article  Google Scholar 

  111. Sofka M, Stewart CV (2006) Retinal vessel centerline extraction using multiscale matched filters confidence and edge measures. IEEE Trans Med Imaging 25:1531–1546

    Article  Google Scholar 

  112. Ricci E, Perfetti R (2007) Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans Med Imaging 26(10):1357–1365

    Article  Google Scholar 

  113. Salem SA, Salem NM, Nandi AK (2007) Segmentation of retinal blood vessels using a novel clustering algorithm (RACAL) with a partial supervision strategy. Med Biol Eng Comput 45(3):261–273

    Article  Google Scholar 

  114. Martinez-Perez ME, Hughes AD, Thom SA, Bharath AA, Parker KH (2007) Segmentation of blood vessels from red-free and fluorescein retinal images. Med Image Anal 11(1):47–61

    Article  Google Scholar 

  115. Martinez-Perez ME, Hughes AD, Thom SA, Parker KH (2007) Improvement of a retinal blood vessel segmentation method using the Insight Segmentation and Registration Toolkit (ITK). In: 29th annual international conference of the IEEE EMBS, vol 1, pp 892–895

  116. Wang L, Bhalerao A, Wilson R (2007) Analysis of retinal vasculature using a multiresolution Hermite model. IEEE Trans Med Imaging 26:137–152

    Article  Google Scholar 

  117. Narasimha-Iyer H, Beach JM, Khoobehi B, Roysam B (2007) Automatic identification of retinal arteries and veins from dual-wavelength images using structural and functional features. IEEE Trans Biomed Eng 54:1427–1435

    Article  Google Scholar 

  118. Espona L, Carreira MJ, Ortega M, Penedo MG (2007) A snake for retinal vessel segmentation. In: Proceedings of the 3rd Iberian conference on pattern recognition and image analysis, vol 4478, pp 178–185

  119. Yang Y, Huang S, Rao N (2008) An automatic hybrid method for retinal blood vessel extraction. Int J Appl Math Comput Sci 18:399–407

    Article  MATH  Google Scholar 

  120. Anzalone A, Bizzarri F, Parodi M, Storace M (2008) A modular supervised algorithm for vessel segmentation in red-free retinal images. Comput Biol Med 38(8):913–922

    Article  Google Scholar 

  121. Farnell DJJ, Hatfield FN, Knox PC, Reakes M, Spencer S, Parry DG et al (2008) Enhancement of blood vessels in digital fundus photographs via the application of multiscale line operators. J Franklin Inst 345:748–765

    Article  MATH  Google Scholar 

  122. Lam BSY, Yan H (2008) A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields. IEEE Trans Med Imaging 27:237–246

    Article  Google Scholar 

  123. Espona L, Carreira MJ, Penedo MG, Ortega M (2008) Retinal vessel tree segmentation using a deformable contour model. In: 19th international conference on pattern recognition (ICPR), vol 1, pp 1–4

  124. Sum KW, Cheung PYS (2008) Vessel extraction under non-uniform illumination: a level set approach. IEEE Trans Biomed Eng 55:358–360

    Article  Google Scholar 

  125. Alonso-Montes C, Vilario DL, Dudek P, Penedo MG (2008) Fast retinal vessel tree extraction: a pixel parallel approach. Int J Circuit Theory Appl 36:641–651

    Article  Google Scholar 

  126. Kande PVS, TS S (2009) Unsupervised fuzzy based vessel segmentation in pathological digital fundus images. J Med Syst 34(5):849–858

    Article  Google Scholar 

  127. Yao C, Jin Chen H (2009) Automated retinal blood vessels segmentation based on simplified PCNN and fast 2D-Otsu algorithm. J Cent S Univ Technol 16:640–646

    Article  Google Scholar 

  128. Cinsdikici DA (2009) Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm. Comput Methods Programs Biomed 96(2):85–95

    Article  Google Scholar 

  129. Lupas CA, Tegolo D, Trucco E (2010) FABC: retinal vessel segmentation using AdaBoost. IEEE Trans Inf Technol Biomed 14(5):1267–1274

    Article  Google Scholar 

  130. Ng J, Clay ST, Barman SA, Fielder AR, Moseley MJ, Parker KH et al (2010) Maximum likelihood estimation of vessel parameters from scale space analysis. Image Vis Comput 28:55–63

    Article  Google Scholar 

  131. Zhang LZ, Karray (2010) Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput Biol Med 4:438–445

    Article  Google Scholar 

  132. Amin M, Yan H (2010) High speed detection of retinal blood vessels in fundus image using phase congruency. Soft Comput Fusion Found Methodol Appl 1:1–14

    Google Scholar 

  133. Lam BSY, Yongsheng G, Liew AWC (2010) General retinal vessel segmentation using regularization-based multiconcavity modeling. IEEE Trans Med Imaging 29:1369–1381

    Article  Google Scholar 

  134. Zhu T (2010) Fourier cross-sectional profile for vessel detection on retinal images. Comput Med Imaging Graph 34:203–212

    Article  Google Scholar 

  135. Sun K, Chen Z, Jiang S, Wang Y (2011) Morphological multiscale enhancement, fuzzy filter and watershed for vascular tree extraction in angiogram. J Med Syst 35(5):811–24

    Article  Google Scholar 

  136. You X, Peng Q, Yuan Y, Cheung YM, Lei J (2011) Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recognit 44:2314–2324

    Article  Google Scholar 

  137. Marin D, Aquino A, Gegundez-Arias ME, Bravo JM (2011) A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans Med Imaging 30(1):146–158

    Article  Google Scholar 

  138. Miri MS, Mahloojifar A (2011) Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction. IEEE Trans Biomed Eng 58:1183–1192

    Article  Google Scholar 

  139. Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG et al (2012) An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans Biomed Eng 59(9):2538–2548

    Article  Google Scholar 

  140. Bankhead P, Scholfield CN, McGeown JG, Curtis TM (2012) Fast retinal vessel detection and measurement using wavelets and edge location refinement. PLoS ONE 7:324–335

    Article  Google Scholar 

  141. Sun K, Chen Z, Jiang S (2012) Local morphology fitting active contour for automatic vascular segmentation. IEEE Trans Biomed Eng 59(2):464–473

    Article  Google Scholar 

  142. Wang Y, Ji G, Lin P, Trucco E (2013) Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition. Pattern Recognit 46(8):2117–2133

    Article  Google Scholar 

  143. Nguyen UTV, Bhuiyan A, Park LAF, Ramamohanarao K (2013) An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recognit 46:703–715

    Article  Google Scholar 

  144. Hou Y (2014) Automatic segmentation of retinal blood vessels based on improved multiscale line detection. J Comput Sci Eng 8(2):119–128

    Article  Google Scholar 

  145. Orlando JI, Blaschko M (2014) Learning fully-connected CRFs for blood vessel segmentation in retinal images. Med Image Comput Comput Assist Interv (MICCAI) 17:634–641

    Google Scholar 

  146. Yin X, Ng BWH, He J, Zhang Y, Abbott D (2014) Accurate image analysis of the retina using Hessian matrix and binarisation of thresholded entropy with application of texture mapping. PLoS ONE 9(4):1–17

    Google Scholar 

  147. Azzopardia G, Strisciuglioa N, Ventob M, Petkova N (2015) Trainable COSFIRE filters for vessel delineation with application to retinal images. Med Image Anal 19(1):46–57

    Article  Google Scholar 

  148. Zhao Y, Rada L, Chen K, Harding SP, Zheng Y (2015) Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images. IEEE Trans Med Imaging 34(9):1797–1807

    Article  Google Scholar 

  149. Roychowdhury S, Koozekanani DD, Parhi KK (2015) Blood vessel segmentation of fundus images by major vessel extraction and subimage classification. IEEE J Biomed Health Inf 19(03):1118–1128

    Google Scholar 

  150. Liskowski P, Krawiec K (2016) Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging 35(11):2369–2380

    Article  Google Scholar 

  151. Li Q, Feng B, Xie L, Liang P, Zhang H, Wang T (2016) A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans Med Imaging 35(01):109–118

    Article  Google Scholar 

  152. Soomro TA, Khan MAU, Gao J, Khan TM, Paul M, Mir N (2016) Automatic retinal vessel extraction algorithm. Int Conf Dig Image Comput Tech Appl (DICTA) 1:1–8

    Google Scholar 

  153. Khan MAU, Soomro TA, Khan TM, Bailey DG, Gao J, Mir N (2016) Automatic retinal vessel extraction algorithm based on contrast-sensitive schemes. Int Conf Image Vis Comput N Z (IVCNZ) 1:1–5

    Google Scholar 

  154. Xinge Y, Qinmu P, Yuan Y, Yiu-ming C, Jiajia L (2011) Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recognit 44:10–11

    Google Scholar 

  155. Khan TM, Khan MA, Kong Y, Kittaneh O (2016) Stopping criterion for linear anisotropic image diffusion: a fingerprint image enhancement case. EURASIP J Image Video Process 6:1–20

    Google Scholar 

  156. Odstrcilik J, Kolar R, Budai A, Hornegger J, Jan J, Gazarek J et al (2013) Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database. IET Image Proc 7(4):373–383

    Article  MathSciNet  Google Scholar 

  157. Meng X, Yin Y, Yang G, Han Z, Yan X (2015) A framework for retinal vasculature segmentation based on matched filters. BioMed Eng OnLine 14(1):1–20

    Article  Google Scholar 

  158. Sreejini KS, Govindan VK (2015) Improved multiscale matched filter for retina vessel segmentation using PSO algorithm. Egypt Inform J 16:253–260

    Article  Google Scholar 

  159. Kumar D, Pramanik A, Kary SS, Maityy SP (2016) Retinal blood vessel segmentation using matched filter and laplacian of gaussian. Int Conf Signal Process Commun (SPCOM) 1:1–5

    Google Scholar 

  160. Hassana G, El-Bendaryb N, Hassanienc AE, Fahmy A, Shoeba AM, Snaself V (2015) Retinal blood vessel segmentation approach based on mathematical morphology. Int Conf Commun Manag Inf Technol (ICCMIT) 65:612–622

    Google Scholar 

  161. Rodrigues J, Bezerra N (2016) Retinal vessel segmentation using parallelb grayscale skeletonization algorithm and mathematical morphology. In: 29th SIBGRAPI conference on graphics, patterns and images, vol 1, pp 17–24

  162. BahadarKhan K, Khaliq AA, Shahid M (2016) A morphological hessian based approach for retinal blood vessels segmentation and denoising using region based otsu thresholding. PLoS ONE 1:1–19

    Google Scholar 

  163. Al-Diri B, Hunter A, Steel D (2009) An active contour model for segmenting and measuring retinal vessels. IEEE Trans Med Imaging 28(9):1488–1497

    Article  Google Scholar 

  164. Karunanayake N, Kodikara ND (2015) An improved method for automatic retinal blood vessel vascular segmentation using gabor filter. J Med Imaging 5:204–213

    Google Scholar 

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Authors and Affiliations

  1. School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW, 2795, Australia

    Toufique Ahmed Soomro & Manoranjan Paul

  2. Discipline of Business Analysis, The University of Sydney Business School, The University of Sydney, Camperdown, NSW, 2006, Australia

    Junbin Gao

  3. Department of Engineering, Macquarie University, Sydney, NSW, 2109, Australia

    Tariq Khan

  4. Centre for Intelligent Signal and Imaging Research Universiti Teknologi, Petronas, Malaysia

    Ahmad Fadzil M. Hani

  5. Department of Electrical and Computer Engineering, Biometric and Sensor Lab, Effat University, Jeddah, Saudi Arabia

    Mohammad A. U. Khan

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  1. Toufique Ahmed Soomro
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  2. Junbin Gao
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  4. Ahmad Fadzil M. Hani
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  5. Mohammad A. U. Khan
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Correspondence to Toufique Ahmed Soomro.

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Soomro, T.A., Gao, J., Khan, T. et al. Computerised approaches for the detection of diabetic retinopathy using retinal fundus images: a survey. Pattern Anal Applic 20, 927–961 (2017). https://doi.org/10.1007/s10044-017-0630-y

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