SpringerLink
Log in

A Review on Automatic Detection of Retinal Lesions in Fundus Images for Diabetic Retinopathy

  • Chapter
  • First Online:
Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems

Abstract

Diabetic retinopathy has emerged as one of the prime reasons for loss of vision. It is diagnosed by analyzing the abnormalities in retinal fundus image. In diabetic patients, the blood vessels become abnormal over time, which results in blockages. These abnormalities lead to the development of various types of aberrations in the retina. High sugar levels make the blood vessels defective and result in formation of bright and dark lesions. The risk of loss of vision is reduced by detecting lesions by analyzing the fundus image in the early stage of diabetic retinopathy. This chapter reviews various studies for automatic abnormality detection in fundus images with a purpose of easing out the work of researchers in the field of diabetic retinopathy. A condensed study on methodology and performance analysis of each detection algorithm is laid out in a simple table form, which can be reviewed effortlessly by any researcher to realize the advantages and shortcomings of each of these algorithms. This review highlights various research protocols for detection and classification of retinal lesions. It provides guidance to researchers working on retinal fundus image processing.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Shankar SR, Jain A, Mitta A (2019) Automated Feature Extraction for Early Detection of Diabetic Retinopathy in Fundus Images. Proceedings of IEEE conference on computer Vision and pattern Recognition; 2009 Jun 20–25; Miami, Florida: USA 2019; pp. 210–17

    Google Scholar 

  2. World Health Organization. Prevention of Blindness and Visual Impairment. Available at http://www.who.int/blindness/causes/priority/en/index6.html

  3. Song J, Lee B (2017) Development of automatic retinal vessel segmentation method in fundus images via convolutional neural networks. Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2017 July 11–15; Jeju Island, Korea 2017; pp. 681–84

    Google Scholar 

  4. Department of Ophthalmology & Visual Sciences. Color Fundus Photography. Available at http://ophthalmology.med.ubc.ca

  5. Non-proliferative Diabetic Retinopathy (NPDR) and Macular Edema. Available at https://louisvillediabeticeyedoctor.com

  6. Net doctor. Explanation of eyes diseases. Available at https://www.netdoctor.co.uk

  7. Matei D, Matei R (2008) Detection of diabetic symptoms in retina images using analog algorithms. World Acad Sci Eng Technol 45:408–411

    Google Scholar 

  8. Mahendran G, Dhanasekaran R, Narmadha KN (2014) Identification of exudates for Diabetic Retinopathy based on morphological process and PNN classifier. Proceedings of the 3rd International Conference on Communication and Signal Processing; 2014 Oct 10–12; Bangkok, Thailand 2014; 1117-21.

    Google Scholar 

  9. American Optometric association. Diabetes and Eye health. Available at https://www.aoa.org/optometrists/tools-and-resources/diabetes-and-eye-health

  10. Decenciere E, Zhang X, Cazuguel G et al (2014) Feedback on a publicly distributed image database: The Messidor database. Image Anal Stereol 33(3):231–234

    Article  Google Scholar 

  11. Kauppi T, Kalesnykiene V, Kamarainen JK, et al (2007) DIARETDB1 diabetic retinopathy database and evaluation protocol. Proceeding of 11th Medical Image Understanding and Analysis; 2007 Jul 17–18; Aberystwyth: Wales 2007; pp. 61–65

    Google Scholar 

  12. Kauppi T, Kalesnykiene V, Kamarainen JK et al (2006) DIARETDB0: Evaluation database and methodology for diabetic retinopathy algorithms. Lappeenranta Univ. Technol, Lappeenranta, Finland, Tech. Rep:1–17

    Google Scholar 

  13. Niemeijer M, Ginneken VB, Cree MJ et al (2010) Retinopathy online challenge: Automatic detection of microaneurysms in digital color fundus photographs. IEEE Trans Med Imaging 29(1):185–195

    Article  PubMed  Google Scholar 

  14. Decenciere E, Cazuguel G, Zhang X et al (2013) TeleOphta Machine learning and image processing methods for Teleophthalmology. IRBM 34(2):196–203

    Article  Google Scholar 

  15. Staal JJ, Abramoff MD, Niemeijer M, Viergever MA, Ginneken BV (2004) Ridge based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501–509

    Article  PubMed  Google Scholar 

  16. Hoover AD, Kouznetsova V, Goldbaum M (2000) Locating Blood Vessels in Retinal Images by Piece-wise Threshold Probing of a Matched Filter Response. IEEE Trans Med Imaging 19(3):203–210

    Article  CAS  PubMed  Google Scholar 

  17. Department Informatik, Department of computer science, High Resolution Fundus (HRF) Image Database Available at http://www.cs.fau.de/research/data/fundusimage

  18. Indian Diabetic Retinopathy Image Dataset. Available at https://doi.org/10.21227/H25W98

  19. Kande GB, Subbaiah PV, Savithri TS, et al (2008) Segmentation of Exudates and Optic Disk in Retinal Images. Proceedings of 6th Indian Conference on Comput Med Imaging Graph; 2008 Dec 16–19; Bhubaneswar: India 2008; pp. 535–42

    Google Scholar 

  20. Sopharak A, Uyyanonvara B, Barman S, Williamson TH (2008) Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Comput Med Imaging Graph 32(8):720–727

    Article  PubMed  Google Scholar 

  21. Jayakumari C, Santhanam T (2008) An intelligent approach to detect hard and soft exudates using echo state neural network. Inf Technol J 7(2):386–395

    Article  Google Scholar 

  22. Saeed E, Szymkowski M, Saeed K, Mariak Z (2019) 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. Sensors 19(3):1–18

    Article  Google Scholar 

  23. Rokade P, Manza R (2015) Automatic detection of hard exudates in retinal images using haar wavelet transform. Intl J Appl Innov Eng 4(5):402–410

    Google Scholar 

  24. Jaafar HF, Nandi AK, Al-Nuaimy W, et al. (2010) Detection of exudates in retinal images using a pure splitting technique. Proceeding of 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology; 2010 Aug 31-Sept 4; Buenos Aires, Argentina 2010; pp. 6745–48

    Google Scholar 

  25. Reza AH, Eswaran E (2011) Diagnosis of diabetic retinopathy: automatic extraction of optic disc and exudates from retinal Images using Marker-controlled Watershed Transformation. J MedSyst 35(6):1491–1501

    Google Scholar 

  26. Rodriguez L, Serrano G (2016) Exudates and blood vessel segmentation in eye fundus images using the fourier and cosine discrete transforms. Computacion y Sistemas 20(4):697–708

    Google Scholar 

  27. Harini R, Sheela N (2016) Feature extraction and classification of retinal images for automated detection of Diabetic Retinopathy. Proceedings of 2nd International Conference on Cognitive Computing and Information Processing; 2016 Aug 12–13; Mysore, Karnataka: India 2016; pp. 1–4

    Google Scholar 

  28. Havaei M, Davy A, Farley DW et al (2017) Brain tumor segmentation with Deep Neural Networks. Med Image Anal 35:18–31

    Article  PubMed  Google Scholar 

  29. Ciresan D, Giusti A, Gambardella LM, Schmidhuber J (2012) Deep neural networks segment neuronal membranes in electron microscopy images. Proceedings of 26th Conference on Neural Information Processing Systems; 2012 Dec 3–8; Lake Tahoe, Nevada: USA 2012; pp. 1–9

    Google Scholar 

  30. Shin H, Roth H, Gao M et al (2016) Deep convolutional neural networks for computera-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298

    Article  PubMed  Google Scholar 

  31. Yu S, **ao D, Kanagasingam Y (2017) Exudate Detection for Diabetic Retinopathy with Convolutional Neural Networks. Proceedings of 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2017 Jul 11–15; Seogwipo, jeju: South Korea 2017; pp. 1744–47

    Google Scholar 

  32. Utami DNQ, Handayani T (2007) Exudates Detection In Retinal Fundus Images Using Combination Of Mathematical Morphology And Renyi Entropy Thresholding. Proceedings of 11th International Conference on Information and Communication Technology and System; 2017 Oct 31; Surabaya, Indonesia 2017; pp. 31–36

    Google Scholar 

  33. Andonova M, Pavlovicova J, Kajan S, Oravec M, Kurilova V (2018) Diabetic retinopathy screening based on CNN. International Symposium ELMAR; 2017 Sept 18–20; Zadar, Croatia 2017; pp. 51–54

    Google Scholar 

  34. Omar M, Tahir MA, Khdifi F. Detection and Classification of Retinal Fundus Images Exudates using Region based Multiscale LBP Texture Approach. Proceedings of International Conference on Control, Decision and Information Technologies; 2016 April 6–8; St. Julian’s, Malta 2016; pp. 227–32

    Google Scholar 

  35. Ruba T, Ramalakshmi K (2015) Identification and segmentation of exudates using SVM classifier. Proceedings of 2nd International Conference on Innovations in Information Embedded and Communication Systems; 2015 March 19–20; Coimbatore, Tamil Nadu: India 2015; pp. 1–6

    Google Scholar 

  36. Kumar A, Abhishek KG, Srivastava M (2012) A segment based technique for detecting exudate from retinal fundus image. Procedia Technol 6:1–9

    Article  CAS  Google Scholar 

  37. Kumar HS, Bharathi PT, Madhuri R (2016) A novel method for image analysis and exudates detection in retinal images. Int J Adv Res Innov 4(1):219–223

    Google Scholar 

  38. Liu Q, Zou B, Chen J, Ke W, Yue K (2017) A location to segmentation strategy for automatic exudates segmentation in colour retinal fundus. Comput Med Imag Graph 55:78–86

    Article  Google Scholar 

  39. Jarmila P, Kajan S, Marko M, Oravec M, Kurilova V (2018) Bright Lesions Detection on Retinal Images by Convolutional Neural Network. Proceedings of 60th International Symposium ELMAR; 2018 Sept 16–19; Zadar, Croatia 2018; pp. 79–82

    Google Scholar 

  40. Kittipol W, Ngiamvibool WS (2018) Automatic detection of exudates in retinal images using region-based, neighborhood and block operation. J ComputSci 14(4):438–452

    Google Scholar 

  41. Jaya T, Dheeba J, Singh NA (2015) Detection of hard exudates in colour fundus images using fuzzy support vector machine based expert system. J Digit Imag 28(6):761–768

    Article  CAS  Google Scholar 

  42. Amel F, Mohammed M, Abdelhafid B (2012) Improvement of the hard exudates detection method used for computer aided diagnosis of diabetic retinopathy. Int J Image Graph Sig process 4(4):19–27

    Google Scholar 

  43. Fraz MM, Jahangir W, Zahid S, Hamayun MM, Barmapn SA (2017) Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification. Biomed Sig Process control 35:50–62

    Article  Google Scholar 

  44. Akram MU, Khalid S, Tariq A, Khan SA, Azam F (2014) Detection and classification of retinal lesions for grading of diabetic retinopathy. Comput Biol Med 5:161–171

    Article  Google Scholar 

  45. Harangi B, Hajdu A (2014) Automatic exudate detection by fusing multiple active contours and region wise classification. Compu tBiol Med 54:156–171

    Article  Google Scholar 

  46. Esmaeili M, Rabbani H, Dehnavi AM, Dehghani A (2012) Automatic detection of exudates and optic disk in retinal images using curvelet transform. IET Image Process 6(7):1005–1013

    Article  Google Scholar 

  47. Prentasic P, Loncaric S (2016) Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion. Comput Methods Prog Biomed 137:281–292

    Article  Google Scholar 

  48. Eadgahi MGF, Pourreza H (2012) Localization of hard exudates in retinal fundus image by mathematical morphology operations. Proceeding of IEEE 2nd International Conference on Computer and Knowledge Engineering; 2012 Oct 18–19; Mashhad, Iran 2012; pp. 185–89

    Google Scholar 

  49. Madheswaran VR, Arthanari M, Sivakumar M (2011) Detection of Diabetic Retinopathy using Radial Basics Function. Int J Innov Tech Creat Eng 1(1):40–47

    Google Scholar 

  50. Zhang X, Chutatape O (2005) Top-down and Bottom-up Strategies in Lesion Detection of Background Diabetic Retinopathy. Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2005 Jun 20–25; pp. 422–28

    Google Scholar 

  51. Osareh A, Mirmehdi M, Thomas B, Markham R (2003) Automated identification of diabetic retinal exudates in digital color images. Br J Ophthalmol 87(10):1220–1223

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Agurto C, Murray V, Barriga E, Murillo S, Pattichis M, Davis H, Russell S, Abramoff M, Soliz P (2010) Multiscale AM-FM methods for diabetic retinopathy lesion detection. IEEE Trans Med Imaging 29(2):502–512

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  54. Franklin SW, Rajan SE (2014) Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images. IET Image Proc 8(10):601–609

    Article  Google Scholar 

  55. Niemeijer M, Ginneken BV, Russell SR, Schulten MSAS, Abràmoff MD (2007) 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

    Article  PubMed  Google Scholar 

  56. Lachure J, Deorankar AV, Lachure S, Gupta S, Jadhav R (2015) Diabetic Retinopathy using morphological operations and machine learning. Proceeding of IEEE International Advance Computing Conference 2015; Jun 12–13; Bangalore, Karnataka: India 2015; pp. 617–22

    Google Scholar 

  57. Acharya UR, Chua CK, Ng EY, Yu W, Chee C (2008) Application of higher order spectra for the identification of diabetes retinopathy stages. J Med Syst 32(6):481–488

    Article  Google Scholar 

  58. Acharya UR, Lim CM, Ng EY, Chee C, Tamura T (2009) Computer-based detection of diabetes retinopathy stages using digital fundus images. Proc Inst MechEng H 223(5):545–553

    Article  CAS  Google Scholar 

  59. Sinthanayothin C, Boyce JF, Cook HL, Lal S (2001) Automated detection of diabetic retinopathy on digital fundus image. Department of ophthalmology 2001, St Thomas’ Hospital, London, U.K. 105–12

    Google Scholar 

  60. Kowsalya N, Kalyani A, Jasmine C, Sivakumar R, Janani M, Ra**ikanth V (2018) An Approach to Extract Optic-Disc from Retinal Image Using K-Means Clustering. Proceeding of 4th International Conference on Biosignals, Images and Instrumentation; 2018 Mar 22–24; Chennai, Tamil Nadu: India 2018. https://doi.org/10.1109/ICBSII.2018.8524655

  61. Shriranjani D, Tebby SG, Satapathy SC, Dey N, Ra**ikanth V (2018) Kapur’s entropy and active contour-based segmentation and analysis of retinal optic disc. Lect Note Elect Eng 490:287–295. https://doi.org/10.1007/978-981-10-8354-9_26

    Article  Google Scholar 

  62. Shree TDV, Revanth K, Raja NSM, Ra**ikanth V (2018) A hybrid image processing approach to examine abnormality in retinal optic disc. Procedia Comput Sci 125:157–166. https://doi.org/10.1016/j.procs.2017.12.022

    Article  Google Scholar 

  63. Sekhar S, Al-Nuaimy W, Nandi AK (2008) Automated localisations of retinal optic disk using Hough transform. 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; 2008 May 14–17; Paris, France 2008; pp. 1577–80

    Google Scholar 

  64. Siddalingaswamy PC, GopalakrishnaPrabhu K (2010) Automatic localization and boundary detection of optic disc using implicit active contours. Int J Comput Appl 1(7):1–7

    Google Scholar 

  65. Tjandrasa H, Wijayanti A, Suciati N (2012) Optic nerve head segmentation using hough transform and active contours. TELKOMNIKA 10(3):531–536

    Article  Google Scholar 

  66. Li H, Chutatape O (2004) Automated feature extraction in color retinal images by a model based approach. IEEE Trans Biomed Eng 51(2):246–254

    Article  PubMed  Google Scholar 

  67. Hoover A, Goldbaum M (2003) Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans Med Imaging 22(8):951–958

    Article  PubMed  Google Scholar 

  68. Mizutani A, Muramatsu C, Hatanaka Y, Suemori S, Hara T, Fujita H (2008) Automated microaneurysm detection method based on double-ring filter in retinal fundus images. Proc. of SPIE on Medical Imaging 2008; Lake Buena Vista (Orlando Area), Florida: United States 2008. https://doi.org/10.1117/12.813468

  69. Dai L, Fang R, Li H, Hou X, Sheng B, Wu Q, Jia W (2018) Clinical report guided retinal microaneurysms detection with multi-sieving deep learning. IEEE Trans Med Imaging 37(5):1–12

    Article  Google Scholar 

  70. Liu J, Shi Y (2011) Image feature extraction method based on shape characteristics and its application in medical image analysis. Proceeding of International Conference on Applied Informatics and Communication; 2011 Aug 20–21; **’an, China 2011; pp. 172–78

    Google Scholar 

  71. Rocha A, Carvalho T, Jelinek HF, Goldenstein S, Wainer J (2012) Points of interest and visual dictionaries for automatic retinal lesion detection. IEEE Trans Biomed Eng 59(8):2244–2253

    Article  CAS  PubMed  Google Scholar 

  72. Xu J, Zhang X, Chen H et al (2018) Automatic analysis of microaneurysms turnover to diagnose the progression of diabetic retinopathy. IEEE Access 6:9632–9642

    Article  Google Scholar 

  73. Seoud L, Hurtut T, Chelbi J, Cheriet F, Langlois PJM (2015) Red lesion detection using dynamic shape features for diabetic retinopathy screening. IEEE Trans Med Imaging 35(4):1116–1126

    Article  PubMed  Google Scholar 

  74. Kumar S, Kumar R, Anuja S, Sahasranamam V (2014) Automatic detection of red lesions in digital colour retinal images. Proceedings of International Conference on Contemporary Computing and Informatics; 2014 Nov 27–29; Mysore, India 2014; pp. 1148–53

    Google Scholar 

  75. Bharali P, Medhi JP, Nirmala SR (2015) Detection of haemorrhages in diabetic retinopathy analysis using colour fundus images. Proceedings of IEEE 2nd International Conference on Recent Trends in Information Systems; 2015 Jul 9–11; Jadavpur University, Kolkat: India 2015; pp. 237–42

    Google Scholar 

  76. Verma K, Prakash D, RamaKrishnan AG (2011) Detection and classification of diabetic retinopathy using retinal images. Proceedings of Annual India IEEE Conference; 2011 Dec 16–18; Hyderabad, India 2011; pp. 1–6

    Google Scholar 

  77. Jaafar HF, Nandi AK, Nuaimy W (2011) Automatic detection of red lesion from digital color fundus photographs. Proceeding of 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology society; 2011 Aug 30-Sept 3; Boston, MA: USA 2011; pp. 6232–35

    Google Scholar 

  78. Orlando JI, Prokofyeva E, Fresno MD, Blaschko MB (2018) An ensemble deep learning based approach for red lesion detection in fundus images. Comput Methods Prog Biomed 153:115–127

    Article  Google Scholar 

  79. Ram K, Joshi GD, Sivaswamy J (2011) A successive clutter- rejection based approach for early detection of diabetic retinopathy. IEEE Trans Biomed Eng 58(3):664–673

    Article  PubMed  Google Scholar 

  80. Quellec G, Lamard M, Josselin PM, Cazuguel G, Cochener B, Roux C (2008) Optimal wavelet transform for the detection of micro aneurysms in retina photographs. IEEE Trans Med Imaging 27(9):1230–1241

    Article  PubMed  PubMed Central  Google Scholar 

  81. Fleming AD, Philip S, Goatman KA, Olson JA, Sharp PF (2006) Automated Microaneurysms detection using local contrast normalization and local vessel detection. IEEE T Med Imaging 25(9):1223–1232

    Article  Google Scholar 

  82. Cree MJ, Olsoni JA, McHardyt KC, Forresters JV (1996) Automated microaneurysms detection. Proceedings of 3rd IEEE International Conference on Image Processing; 1996 Sept 19; Lausanne, Switzerland 1996; pp. 699–702

    Google Scholar 

  83. Sekar GB, Nagarajan P (2012) Localization of optic disc in fundus images by using clustering and histogram techniques. Proceedings of 12th IEEE International Conference on Computing Electronics and Electrical Technologies; 2012 March 21–22; Kumaracoil, Kanyakumari. Tamilnadu: India 2012; pp. 584–89

    Google Scholar 

  84. Walter T, Massin P, Erginay A, Ordonez R, Jeulin C, Klein JC (2017) Automatic detection of microaneurysm in color fundus images. Med Image Anal 11(6):555–566

    Article  Google Scholar 

  85. Shah SAA, Laude A, Faye I, Tang TB (2016) Automated Microaneurysms detection in diabetic retinopathy using curvelet transform. J Biomed Opt 21(10):101404–101410

    Google Scholar 

  86. Zhou W, Wu C, Chen D, Wang Z, Yi Y, Du W (2017) Automatic microaneurysms detection based on multifeature fusion dictionary learning. Comput Math Methods Med:1–11. https://doi.org/10.1155/2017/2483137

  87. Srivastava R, Wong DWK, Duan L, Liu J, Wong TY (2015) Red lesion detection in retinal fundus images using Frangi-based filters. Proceedings on 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2015 Aug 25–29; Milan, Italy 2015; pp. 5663–66

    Google Scholar 

  88. Renzhen W, Meng D, Chen B, Wang L (2019) Weakly supervised lesion detection from fundus images. IEEE Trans Med Imaging 38(6):1501–1512

    Article  Google Scholar 

  89. Tang L, Niemeijer M, Reinhardt JM, Garvin MK, Abramoff M (2013) Splat feature classification with application to retinal hemorrhage detection in fundus images. IEEE Tran Med Imaging 32(2):364–375

    Article  Google Scholar 

  90. Adal KM, Etten VP, Martinez JP, Rouwen KW, Vermeer KA, Vliet LJ (2017) An automated system for the detection and classification of retinal changes due to red lesions in longitudinal fundus images. IEEE Trans Biomed Eng 65(6):1382–1390

    Article  PubMed  Google Scholar 

  91. Kande GB, Savithri T, Subbaiah VP, Tagore MRM (2009) Detection of red lesions in digital fundus images. IEEE International Symposium on Biomed Imaging: From Nano to Macro 2009; Jun 28-Jul 1; Boston, MA. USA 2009; pp. 558–61

    Google Scholar 

  92. 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. 30th Annual International Conference of IEEE Eng in Med and Bio society; 2008 Aug 20–25; Vancouver, BC, Canada: pp. 5425–28

    Google Scholar 

  93. Sopharak A, Uyyanonvara B, Barman S (2009) Automatic exudate detection from non-dilated diabetic retinopathy retinal images using fuzzy C-means clustering. J Sensors 9(3):2148–2161

    Article  Google Scholar 

  94. Ram K, Sivaswamy J (2009) Multi-space clustering for segmentation of exudates in retinal color photographs. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2009 Sept 3–6; MN, USA 2009; pp. 1437–40

    Google Scholar 

  95. Annunziata R, Garzelli A, Ballerini L, Mecocci A, Trucco E (2016) Leveraging multiscale hessian-based enhancement with a novel exudate in painting technique for retinal vessel segmentation. IEEE J Biomed Health Inform 20(4):1129–1138

    Article  PubMed  Google Scholar 

  96. Zhou W, Wu C, Yi Y, Du W (2017) Automatic Detection of Exudates in Digital Color Fundus Images Using Super Pixel Multi-Feature Classification. IEEE ACCESS 5:17077–17088

    Article  Google Scholar 

  97. Amiri SA, Hassanpour H, Shahiri M, Ghaderi R (2012) Detection of microaneurysms in retinal angiography images using the circular Hough transform. J Adv Comp Res 3(1):1–12

    CAS  Google Scholar 

  98. Mane VM, Kawadiwale RB, Jadhav D (2015) Detection of red lesions in diabetic retinopathy affected fundus images. Proceedings of International Advance Computing Conference; 2015 Jun 12–13; Bangalore, Karnataka: India 2015; pp. 56–60

    Google Scholar 

  99. Pratt H, Coenen F, Broadbent DM, Harding SP, Zheng Y (2016) Convolutional neural networks for diabetic retinopathy. Procedia Comp Sci 90:200–205

    Article  Google Scholar 

  100. Soares I, Branco MC, Pinheiro AM (2014) Microaneurysms detection using a novel neighborhood analysis. Proceedings of Ophthalmic Medical Image Analysis International Workshop; 2014 Sept 14; Boston, Massachusetts 2014; pp. 65–72

    Google Scholar 

  101. Narasimhan K, Neha VC, Vijayarekha K (2012) An efficient automated system for detection of diabetic retinopathy from fundus images using support vector machine and bayesian classifiers. Proceeding of IEEE International Conference on Computing, Electronics and Electrical Technologies; 2012 March 21–22; Kumaracoil, Tamil Nadu: India 2012; pp. 964–69

    Google Scholar 

  102. Figueiredo I, Kumar S, Oliveira C, Ramos J, Engquist B (2015) Automated lesion detectors in retinal fundus images. Comput Biol Med 66:47–65

    Article  CAS  PubMed  Google Scholar 

  103. Kar SS, Maity SP (2018) Automatic detection of retinal lesions for screening of diabetic Retinopathy. IEEE Trans Biomed Eng 65(3):608–618

    Article  PubMed  Google Scholar 

  104. Sinthanayothin C, Boyce SJF, Williamson TH, Cook HL (2002) Automated detection of diabetic retinopathy on digital fundus image. Diabet Med 19(2):105–112

    Article  CAS  PubMed  Google Scholar 

  105. Roy R, Aruchamy S, Bhattacharjee P (2013) Detection of retinal microaneurysms using fractal analysis and feature extraction technique. Proceedings of IEEE International Conference on Communication and Signal Processing; 2013 Jun 3–5; Melmaruvathur, Tamil Nadu: India 2013; pp: 469–74

    Google Scholar 

  106. Adarsh P, Jeyakumari D (2013) Multiclass SVM-based automated diagnosis of diabetic retinopathy. Proceeding of IEEE International Conference on Communication and Signal Processing; 2013 April 3–5; Melmaruvathur, Tamil Nadu: India 2013; pp. 206–10

    Google Scholar 

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

    Article  Google Scholar 

  108. Niemeijer M, Ginneken BV, Staal J, Schulten MSAS, Abramoff MD (2005) Automatic detection of red lesions in digital color fundus photographs. IEEE Trans Med Imaging 24(5):584–592

    Article  PubMed  Google Scholar 

  109. Sanchez CI, Hornero R, Mayo A, Garcia M (2009) Mixture model-based clustering and logistic regression for automatic detection of microaneurysms in retinal images. Proceedings of SPIE medical imaging; 2009 Mar 3; Lake Buena Vista (Orlando Area), Florida. United States 2009; pp. 72601–608

    Google Scholar 

  110. Salem S, Salem N, Nandi A (2007) Segmentation of retinal blood vessels using a novel clustering algorithm (RACAL) with a partial supervision strategy. Med Biol Eng Comput 45:261–273

    Article  PubMed  Google Scholar 

  111. Li Q, Feng B, **e L, Liang P, Zhang H, Wang T (2016) A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans Med Imaging 35(1):109–118

    Article  PubMed  Google Scholar 

  112. Song J, Lee B (2017) Development of automatic retinal vessel segmentation method in fundus images via convolutional neural networks. 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2017 Jul 11–15; Seogwipo, Seogwipo, South Korea 2017; pp. 681–84

    Google Scholar 

  113. Akram MU, Tariq A, Nasir S, Khan SA (2009) Gabor wavelet based vessel segmentation in retinal images. Proceedings IEEE Symposium on Computational Intelligence for Image Processing; 2009 April 2; Nashville, TN. USA 2009; pp 116–19

    Google Scholar 

  114. Fazli S, Samadi S, Nadirkhanlou P (2013) A novel retinal vessel segmentation based on local adaptive histogram equalization. 8th Iranian conference on machine vision and image processing. 2013 Sept 10–12; Zanjan, Iran 2013; pp. 10–12

    Google Scholar 

  115. Chakraborti T, Dhiraj KJ, Chowdhury AS (2014) A self-adaptive matched filter for retinal blood vessel detection. Mach Vis Appl 26(1):55–68

    Article  Google Scholar 

  116. Zhang B, Zhang L, Karray F (2010) Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput Biol Med 40(4):438–445

    Article  PubMed  Google Scholar 

  117. ** functions. Proc IEEE Int Conf Bioinform Biomed:15–18. https://doi.org/10.1109/BIBM.2016.7822573

  118. Keerthana K, Jayasuriya TJ, Raja NSM, Ra**ikanth V (2017) Retinal vessel extraction based on firefly algorithm guided multi-scale matched filter. Int J Modern Sci Tech 2(2):74–80

    Google Scholar 

  119. Khan KB, Khaliq AA, Shahid M (2016) A morphological hessian based approach for retinal blood vessels segmentation and denoising using region based otsu thresholding. PLOS ONE

    Google Scholar 

  120. Marino C, Penedo MG, Barreira N, Ares E, Ortega M, Gomez-Ulla F (2008) Automated three stage red lesions detection in digital color fundus images. WSEAS T Comp 7(4):207–215

    Google Scholar 

  121. Zhu W, Zeng N, Wang N. Sensitivity, Specificity, Accuracy, Associated Confidence Interval and ROC Analysis with Practical SAS Implementations. Proceedings of Health Care and Life Sciences; 2010 Nov 14–17; Baltimore, Maryland. NESUG 2010

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ECE department, JNTUA, Ananthapuramu, Andhra Pradesh, India

    Remya Koppara Revindran & Mahendra Nanjappa Giriprasad

Authors
  1. Remya Koppara Revindran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mahendra Nanjappa Giriprasad
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Electronics and Communication Engineering, Sri Sairam Engineering College, Chennai, Tamil Nadu, India

    E. Priya

  2. Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, India

    V. Ra**ikanth

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Koppara Revindran, R., Nanjappa Giriprasad, M. (2021). A Review on Automatic Detection of Retinal Lesions in Fundus Images for Diabetic Retinopathy. In: Priya, E., Ra**ikanth, V. (eds) Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-6141-2_10

Download citation

Publish with us

Policies and ethics

Search

Navigation