Skip to main content
SpringerLink
Log in

Progress towards automated diabetic ocular screening: A review of image analysis and intelligent systems for diabetic retinopathy

  • Review
  • Published:
Medical and Biological Engineering and Computing Aims and scope Submit manuscript

Abstract

Patients with diabetes require annual screening for effective timing of sight-saving treatment. However, the lack of screening and the shortage of ophthalmologists limit the ocular health care available. This is stimulating research into automated analysis of the reflectance images of the ocular fundus. Publications applicable to the automated screening of diabetic retinopathy are summarised. The review has been structured to mimic some of the processes that an ophthalmologist performs when examining the retina. Thus image processing tasks, such as vessel and lesion location, are reviewed before any intelligent or automated systems. Most research has been undertaken in identification of the retinal vasculature and analysis of early pathological changes. Progress has been made in the identification of the retinal vasculature and the more common pathological features, such as small aneurysms and exudates. Ancillary research into image preprocessing has also been identified. In summary, the advent of digital data sets has made image analysis more accessible, although questions regarding the assessment of individual algorithms and whole systems are only just being addressed.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Aiello, L., Cavallerano, J., Gardner, T., King, G., Blankenship, G., Ferris, F., andKlein, R. (1998a): ‘Diabetic retinopathy’,Diabetes Care,21, pp. 143–156

    Google Scholar 

  • Aiello, L., Bursell, S., Cavallerano, J., Kelly Gardner, W., andStrong, J. (1998b): ‘Joslin vision network validation study: Pilot image stabilization phase’,J. Am. Optometr. Assoc.,69, pp. 699–710.

    Google Scholar 

  • Akita, K., andKuga, H. (1979): ‘Towards understanding color ocular fundus images’.Proc. 6th IJCAI, Tokyo, Japan, pp. 7–12

  • Akita, K., andKuga, H. (1982): ‘A computer method of understanding ocular fundus images’,Pattern Recognit.,16, pp. 431–443

    Google Scholar 

  • Aldington, S., Kohner, E., Meuer, S., Klein, R., andSjole, A.: (1995): ‘Methodology for retinal photography and assessment of diabetic retinopathy: the EURODIAB IDDM complications study’,Diabetologia,38, pp. 437–444

    Google Scholar 

  • Aleynikov, S., andMicheli-Tzanakuo, E. (1988): ‘Classfication of retinal damage by a neural network based system’,J. Med. Syst.,22, pp. 129–136

    Google Scholar 

  • American Diabetes Association (1999): ‘American diabetes association: Clinical practice recomendations 1999’. Technical Report S1, American Diabetes Association

  • Ballerini, L. (1998): ‘Integration of retinal image sequences’, Proceedings of SPIE Conference on Applications of Digital Image Processing XXI.3460, pp. 237–248

    Google Scholar 

  • Ballerini, L. (1999): ‘Detection and quantification of diabetic retinopathy’. Proceedings of SPIE Conference on Applications of Digital Image Processing XXII,3808, pp. 213–223

    Google Scholar 

  • Barrett, S., Jerath, M., Rylander III, H., andWelch, A. (1994). ‘Digital tracking and control of retinal images’,Optical Eng.,33, pp. 150–159.

    Google Scholar 

  • Baxt, W. G. (1994): ‘A neural network trained to identify the presence of myocardial infarction bases some decisions on clinical associations that differ from accepted clinical teaching’,Med. Decis. Making,14, pp. 217–222

    Google Scholar 

  • Baxt, W. G., andSkora, J. (1996): ‘Prospective validation of artificial neural network trained to identify acute myocardial infarction see comments]’,Lancet,347, pp. 12–15

    Article  Google Scholar 

  • Becker, D., Can, A., Turner, J., Tannenbaum, H., andRoysam, B. (1998): ‘Image processing algorithms for retinal montage synthesis, mapping, and real-time location determination’,IEEE Trans. Biomedical Eng.,45, pp. 105–117

    Google Scholar 

  • Berestov, A. (2000): ‘Stereo fundus photography: automatic evaluation of retinal topography’. Proceedings of SPIE Conference on Stereoscopic Displays and Virtual Reality Systems VII′, pp. 50–59

  • Berry, M., andWesterman D. (1998): ‘Cluster form analysis techniques for diabetic retinopathy’,Proc. Math. Models Med. Health Sci., pp. 35–50

  • Can, A., Shen H., Turner, J., Tanenbaum, H., andRoysam, B. (1999): ‘Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms’,IEEE Trans. Inform. Technol. Biomed.,3, pp. 125–138

    Google Scholar 

  • Casi, E., Ceravola, A., Cionini, R., Sperduti, A., Starita, A., andViti, S. (1996): ‘Diabetic retina analysier’ inBoom, H. (Ed): ‘Proceedings of 18th Annual International Conference of IEEE Engineering in Medicine and Biology Society’ (Amsterdam, The Netherlands), pp. 1128–1129

  • Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., andGoldbaum, M. (1989a): ‘Detection of blood vessels in retinal images using two-dimensional matched filters’IEEE Trans. Med. Imag.,8, pp. 263–269

    Google Scholar 

  • Chaudhuri, S., Chatterjee, S., Katz, N., andGoldbaum, M. (1989b): ‘Automatic detection of the optic nerve in retinal images’ IEEE International Conference on Image Processing, Singapore, Vol. 1 pp. 1–5

    Google Scholar 

  • Cideciyan, A., Jacobson, S., Kemp, C., Knighton, R., andNagel, J. (1992): ‘Reglstration of high resolution images of the retina’, Proceedings of SPIE Conference on Medical Imaging VI: Image Processing,1652, pp. 310–322

    Google Scholar 

  • Cree, M., Olson, J., McHardy, K., Sharp, P., andForrester, J. (1997): ‘A fully automated comparitive microaneurism digital detection system’,Eye,11, pp. 622–628

    Google Scholar 

  • Dasbach, E., Fryback, D., Newcomb, P., Klein, R., andKlein, B. (1991): ‘Cost-effectiveness of strategies for detecting diabetic retinopathy’,Med. Care,29, pp. 20–39

    Google Scholar 

  • Daxer, A. (1993a): ‘Characterization of the neovascularisation process in diabetic retinopathy by means of fractal geometry: diagnostic implications’,Graefe's Arch. Clin. Exp. Ophthalmol.,231, pp. 681–686

    Article  Google Scholar 

  • Daxer, A. (1993b): ‘The fractal geometry of proliferative diabetic retinopathy: implications for the diagnosis and the process of retinal vasculogenesis’,Current Eye Res.,12, pp. 1103–1109

    Google Scholar 

  • DCCT Research Group (1995): ‘Progression of retinopathy with intensive versus conventional treatment in the Diabetes Control and Complications Trial’,Ophthalmology,102, pp. 647–661

    Google Scholar 

  • Diabetes UK (2000): ‘Diabetes in the UK-the missing million’. The British Diabetes Association

  • Domingo, J., Ayala, G., Simo, A., Deves, E., Martinez Costa, L., andMarco, P. (1997): ‘Irregular motion recovery in fluorescein angiograms’,Pattern Recognit. Lett.,18, pp. 805–821

    Article  Google Scholar 

  • Donohoe, G., Soliz, P., andNemeth, S. (1998): ‘Computer-aided image analysis for background diabetic retinopathy’, Proceedings of SPIE Conference on Medical Imaging 1998: Image Processing.3338, pp. 1017–1027

    Google Scholar 

  • Donohoe, G., Nemeth, S., andSoliz, P. (1999): ‘An interactive system for computer-aided retinal image analysis’. 12th IEEE Symposium on Computer Based Medical Systems, 306, Los Alamitos, pp. 184–189

  • DRS Research Group (1981): ‘Report 7. a modification of the airlie house classification of diabetic retinopathy’,Invest. Opthalmol. Vis. Sci.,21, pp. 210–226

    Google Scholar 

  • DRS Research Group (1987): ‘Indfcations for photocoagulation treatment of diabetic retinopathy: Diabetic retinopathy study report number 14’,27, pp. 239–253

    Google Scholar 

  • Ege, B., Hejlesen, O., Larsen, O., Jennings, B., Kerr, D., andCavan, D. (1998): ‘Screening for diabetic retinopathy using computer based image analysis and bayesian classification’,Diabetes. Nutrit. Metabol.,11, p. 95

    Google Scholar 

  • Ege, B., Hejlesen, O., Larsen, O. V., M., Jennings, B., Kerr, D., andCavan, D. (2000): ‘Screening for diabetic retinopathy using computer based image analysis and statistical classification’,Comput. Methods Progr. Biomed.,62, pp. 165–175

    Google Scholar 

  • ETDRS Research Group (1985): “Photocoagulation for diabetic macular edema: Early treatment diabetic retinopathy study report number l’,Arch. Ophthalmol.,103, pp. 1796–1806

    Google Scholar 

  • ETDRS Research Group (1991a): ‘Grading diabetic retinopathy from stereoscopic color fundus photographs-an extension of the modified Airlie House classification: Early treatment diabetic retinopathy study report number 10’,Ophthalmology,98, pp. 786–806

    Google Scholar 

  • ETDRS Research Group (1991b): ‘Early photocoagulation for diabetic retinopathy: Early treatment diabetic retinopathy study report number 9’,Ophthalmology,98, pp. 766–785

    Google Scholar 

  • Fisher, R., Scott, J., andPalmer, E. (1996): ‘Neural networks in ventilation-perfusion imaging; part 1. Effects of interpretive criteria and network architecture’,Radiology,198, pp. 699–706

    Google Scholar 

  • Frame, A., Cree, M., Olson, J., Undrill, P., Mchardy, K., Sharp, P., andForrester, J. (1996): ‘Computer based classification of retinal micro-aneurysms’. International Conference on Neural Networks and Expert Systems in Medicine and Health Care, plymouth, United Kingdom, pp. 50–56

  • Frame, A., Undrill, P., Olson, J., McHardy, K., Sharp, P., andForrester, J. (1997a): ‘Struetural analysis of retinal vessels’ Proceedings of 6th International Conference on Image Processing and its Applications, IEE, Dublin, Vol. 2, pp. 824–827

    Google Scholar 

  • Frame, A., Undrill, P., Olson, J., McHardy, K., Sharp, P., andForrester, J. (1997b): ‘Texture analysis of retinal neovascularisation’, Proceedings of IEE Colloquium on Pattern Recognition, London, Vol. 6, pp. 1–6

    Google Scholar 

  • Gao, X., Bharath, A. A., Hughes, A., A., S., Chapman, N., andThom, S. (1997): ‘Towards retinal vessel parameterisation’. Proceedings of SPIE Conference on Medical Imaging,3034, pp. 734–744

    Google Scholar 

  • Gardner, G., Keating, D., Williamson, T., andElliot, A. (1996): ‘Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool’,Br. J. Ophthalmol.,80, pp. 940–944

    Google Scholar 

  • George, L., Leverton, C., Young, S., Lusty, J., Dunstan, F., andOwens, D. (1997): ‘Can digitised colour 35 mm transparencies be used to diagnose diabetic retinopathy?’,Diabet. Med.,14, pp. 970–973

    Google Scholar 

  • Goh, K., Sarkodie-Gyan, T., Campbell, A., Simpson, D., andMcNeela, B. (1997): ‘Computer assisted photocoagulation for treatment of diabetic retinopathy: preliminary results’. Proceedings of 30th International Symposium on Automotive Technology and Automation, Florence, Vol. 2, pp. 655–662.

    Google Scholar 

  • Goldbaum, M., Katz, N., Chaudhuri, S., andNelson, M. (1989): ‘Image understanding for automated retinal diagnosis’. Proceedings of IEEE Symposium for Computer Applications in Clinical Medicine, pp. 756–760.

  • Goldbaum, M., Katz, N., Chaudhuri, M., Nelson, M., andKube, P. (1990a): ‘Digital image processing for ocular fundus images’,Ophthalmol. Clin. North Am.,3, pp. 447–466

    Google Scholar 

  • Goldbaum, M., Katz, N., Nelson, M., andHaff, L. (1990b): ‘The discrimination of similarly colored objects in computer images of the ocular fundus’,Invest. Ophthal. Vis. Sci.,31, pp. 617–623

    Google Scholar 

  • Goldbaum, M., Kouznetsova, V., Cote, B., Hart, W., andNelson, M. (1993): ‘Automated registration of digital ocular fundus images for comparison of lesions’,SPIE: Ophthalmic Technol. III,1877, pp. 94–99

    Google Scholar 

  • Goldbaum, M., Jain, R., Gupta, A., Moezzi, S., Taylor, A., Chatterjee, S., andBurgess, S. (1996): ‘Database for ocular fundus images ranked by semantic similarity’,Investigat. Ophthalmol. Vis. Sci.,37, p. S957

    Google Scholar 

  • Gonzalez, R., andWoods, R. (1992): ‘Digital image processing’ (Addison-Wesley, 1992)

  • Gregson, P., Shen, Z., Scott, R., andKozoudek, V. (1995): ‘Automated grading of venous beading’,Comput. Biomed. Res.,28, pp. 291–304

    Article  Google Scholar 

  • Hammer, M., Leistritz, S., Leistritz, L., Schweitser, D., Thamm, E., andDonnerhacke, K. H. (1997): ‘Monte-Carlo simulation of retinal vessel profiles for the interpretation ofin vivo oxymetric measurements by imaging fundus reflectometry’, Proceedings on Medical Applications of Lasers in Dermatology. Ophthalmology, Dentistry, and Endoscopy, San Remo, Italy, Vol. 3192, pp. 211–218

    Google Scholar 

  • Hart, W., Goldbaum, M., Cote, B., Kube, P., andNelson, M. (1997): ‘Automated measurement of retinal vascular tortuosity’. Proceedings of American Medical Informatics Association Annual Fall Symposium, Nashville, Vol. 63, pp. 459–463

    Google Scholar 

  • Henricsson, M., Karlson, C., Ekholm, L., Kaikkonen, P., Sellman, A., Steffert, E., andTyrberg, M. (2000): ‘Colour slides or digital photography in diabetes screening—a comparison’,Acta Ophthalmologica Scand.,78, pp. 164–168

    Google Scholar 

  • Hipwell, J., Strachnt, F., Olson, J., McHardy, K., Sharp, P., andForrester, J. (2000): ‘Automated detection of microaneurysms in digital red-free photographs: a diabetic retinopathy screening tool’,Diabet. Med.,17, pp. 588–594

    Article  Google Scholar 

  • Hoover, A., andGoldbaum, M. (1998a): ‘Fuzzy convergence’. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Santa Barbara, California, pp. 716–721

  • Hoover, A., Kouznetsova, V., andGoldbaum, M. (1998b): ‘Location blood vessels in retinal images by piece-wise threshold probing of a matched filter response’. American Medical Informatics Association Conference, Orlando, pp. 931–935

  • Iioover, A., Kouznetzova, V., andGoldbaum, M. (2000): ‘Location blood vessels in retinal images by piece-wise threshold probing of a matched fileter response’,IEEE Trans. Med. Imaging.,19, pp. 203–210

    Google Scholar 

  • Hunter, A., Lowell, J., Owens, J., Kennedy, L., andSteele, D. (2000): ‘Quantification of diabetic retinopathy using neural networks and sensitivity analysis’. Proceedings of Artificial Neural Networks in Medicine and Biology, Götenborg, Sweden, pp. 81–86

  • Iliasova, N., Ustinov, A., Branchevsky, S., andDurasov, A. (1998): ‘Methods for estimating geometric parameters of retinal vessels using diagnostic images of fundus’, Proceedings of SPIE Conference on Optical Information Science and Computer and Holographic Optics and Image Processing,3460, pp. 316–325

    Google Scholar 

  • Jasiobedski, P., Taylor, C., andBrunt, J. (1993): ‘Automated analysis of retinal images’,Image Vision Comput.,11, pp. 139–144

    Google Scholar 

  • Javitt, J., Aiello, L., Chiang, Y., Ferris, F., Canner, J., andGreenfield, S. (1994): ‘Preventive eye care in people with diabetes is cost-saving to the federal government’,Diabetes Care,17, pp. 909–917

    Google Scholar 

  • Katz, N., Goldbaum, M., Nelson, M., andChaudhuri, S. (1988): ‘An image processing system for automatic retina diagnosis’. Proceedings of SPIE Conference on Three-Dimensional Imaging and Remote Sensing Imaging, pp. 131–137

  • Klein, R., Klein, B., Magli, Y., Brothers, R., Meuer, S., Moss, S., andDavis, M. (1986): ‘An alternative method of grading diabetic retinopathy’,Ophthalmology,93, pp. 1183–1187

    Google Scholar 

  • Klein, R., Moss, S., Klein, B., Davis, M., andDemets, D. (1989): ‘The Wisconsin epidemiologic study of diabetic retinopathy: XI The incidence of macular edema’,Ophthalmology,96, pp. 1501–1510

    Google Scholar 

  • Kochner, B., Schulman, D., Obermaier, M., Zahlmann, G., Mann, G., andEnglmeier, K. H. (1997): ‘An image processing system for analysing color fundus photographs with regard to diabetic retinopathy’,Klinische Monatsblatter fur Augenheilkunde,211, p. 11

    Google Scholar 

  • Kochner, B., Schuhmann, D., Michaelis, M., Mann, G., andEnglemeier, K.-H. (1998): ‘Course tracking and contour extraction of retinal vessels from color fundus photographs: most efficient use of steerable filters for model based image analysis’. Proceedings of SPIE Conference on Medical Imaging, pp. 755–761

  • Kozousek, V., Shen, Z., Gregson, P., andScott, R. (1992): ‘Automated detection and quantification of venous beading using fourier analysis’,Can. J. Ophthalmol.,27, pp. 288–294

    Google Scholar 

  • Kristinsson, J., Gottfredsdottir, M., andStefansson, E. (1997): ‘Retinal vessel dilation and elongation precedes diabetic macular oedema’,Br. J. Ophthalmol.,81, pp. 274–278

    Google Scholar 

  • Landini, G., Misson, G., andMurray, P. (1993): ‘Fractal analysis of the normal human retinal flourescin angiogram’,Current Eye Res.,12, pp. 23–27

    Google Scholar 

  • Landini, G., Murray, P., andMisson, C. (1995): ‘Local connected fractal dimensions and lacunarity analysis of 60 fluorecin angiograms’,Investigat. Ophthalmol. Visual Sci.,36, pp. 2749–2755

    Google Scholar 

  • Lee, S. (1992): ‘Identifying retinal vessel networks in ocular fundus images’, PhD thesis, The University of New Mexico, USA

    Google Scholar 

  • Lee, S., andWang, Y. (1998): ‘A general algorithm of recognizing small, vague and imager-alike objects in a nonuniformly illuminated medical diagnostic image’. Proceedings of 32nd Conference on Signals Systems and Computers.2, Asilomar, pp. 941–943

    Google Scholar 

  • Lee, S., andWang, Y. (1999): ‘Automatic retinal image quality assessment and enhancement’, SPIE Conference on Image Processing,3661, pp. 1581–1590

    Google Scholar 

  • Lee, S., Wang, Y., andLee, E. (1999): ‘A computer algorithm for automated detection and quantification of microanuerysms and hemorrhages (hma's) in color retinal images’, SPIE Conference on Image Perception and Performance,3663, pp. 61–71

    Google Scholar 

  • Liesenfeld, B., Kohner, E., Piehlmeier, W., Kluthe, S., Porta, M., Bek, T., Obermaier, M., Mayer, H., Mann, G., Holle, R., andHep, K. (2000): ‘A telemedical approach to the screening of diabetic retinopathy: Digital fundus photography’,Diabetes Care,23, pp. 345–348

    Google Scholar 

  • Martínez-Pérez, M., Hughes, A., Stanton, A., Thom, S., Bharath, A., andParker, K. (1990a): ‘Segmentation of retinal blood vessels based on the second directional derivative and region growing’. Proceedings of International Conference on Image Processing, New Jersey, USA, pp. 173–176

  • Martínez-Pérez, M., Hughes, A., Stanton, A., Thom, S., Bharath, A., andParker, K. (1999b): ‘Retinal blood vessel segmentation by means of scale-space analysis and region growing’, Proceedings of 2nd International Conference on Medical Image Computing and Computer Assisted Intervention, Cambridge, UK, pp. 90–97

  • Mendonça, A., Campilho, A., andNunes, J. (1999): ‘Automatic segementation of microaneurysms in retinal angiograms of diabetic patients’, Proceedings of 10th International Conference on Image Analysis and Processing, Los Alamitos, pp. 728–723

  • National Screening Committee (2000): ‘Second report of the United Kingdom national screening committee’, Department of Health, PO Box 777, London SE1 6XH, United Kindom.

    Google Scholar 

  • Nguyen, H., Buttler, M., Roychoudhry, A., Shannon, A., Flack, J., andMitchel, P. (1996): ‘Classification of diabetic retinopathy using neural networks’, Proceedings of 18th Annual International Conference of IEEE Engineering in Medicine and Biology Society, IEEE, Amsterdam, The Netherlands, Vol. 4, pp. 1548–1549

    Google Scholar 

  • Øien, G., andOsnes, P. (1995): ‘Diabetic retinopathy: Automatic detection of early symptoms from retinal images’, Proceedings of Norwegian Signal Processing Symposium, Stavanger, Norway, Vol. VII, pp. 135–140

    Google Scholar 

  • Okazaki, K., andTamura, S. (1983): ‘Spherical shading correction of eye fundus image’. Proceedings of International Conference on Systems, Man and Cybernetics, Bombay, India, pp. 1084–1087

  • Otsu, N. (1979): ‘A threshold method from gray-level histograms’,IEEE Trans. Syst. Man Cybern.,9, pp. 62–66

    Google Scholar 

  • Panico, J., andSterling, P. (1995): ‘Retinal neurons and vessels are not fractal but space-filling’.J. Compar: Neurol.,361 pp. 479–490

    Article  Google Scholar 

  • Patel, V. (1995): ‘Diabetic retinopathy: haemodynamic and clinical factors in the pathogenesis’, (Verlag, Josef Eul, Koln, 1995: (ISBN): 3-89012-432-1)

    Google Scholar 

  • Peli, E. (1993): ‘Enhancement of retinal images: Pros and problems’,Neurosci. Biobehav. Rev.,17, pp. 477–482

    Article  Google Scholar 

  • Phillips, R., Spencer, T., Ross, P., Sharp, P., andForrester, J. (1991): ‘Quantification of diabetic maculopathy by digital imaging of the fundus’,Eye,5, pp. 130–137

    Google Scholar 

  • Poli, R., andValli, G. (1997): ‘An algorithm for real-time vessel enhancement and detection’,Comput. Methods Programs Biomed.,52, pp. 1–22

    Article  Google Scholar 

  • Rassam, S., Patel, V., Brinchmann-Hansen, O., Engvold, O., andKohner, E. (1994): ‘Acourate vessel width measurement from fundus photographs: a new concept’,Br. J. Ophthalmol. 78, pp. 24–29

    Google Scholar 

  • Ryan, N., andHeneghan, C. (1999): ‘Image registration techniques for digital ophthalmic images’, Irish Signal and Systems Conference, Galway, Ireland, pp. 301–308

  • Schalkofe, R. (1989): ‘Digital image processing and computer vision’, (Wiley and Sons, Inc., New York, 1989)

    Google Scholar 

  • Scott, J., Fisher, R., andPalmer, E. (1996): ‘Neural networks in ventilation-perfusion imaging; part ii. Effects of interpretive variability’,Radiology,198, pp. 707–713

    Google Scholar 

  • Shin, D., Kaiser, R., Lee, M., andBerger, J. (1999): ‘Fundus image change analysis:geometric and radiometric normalisation’, Proceedings of SPIE Conference on Ophthalmic Technologies,3591 San Jose, California, pp. 129–136

    Google Scholar 

  • Sinclair, S., Gupta, A., andBhasin, S. (1996): ‘Atitomated lesion detection and grading of retinopathy from fundus photographs of diabetics’,Diabetes,45, p. 192A

    Google Scholar 

  • Sinthanayothin, C., Boyce, J., Cook, H., andWilliamson, T. (1999): ‘Automated localisation of the optic disk, fovea, and retinal blood vessels from digital colour fundus images’,Br: J. Ophthalmol.,83, pp. 902–910

    Google Scholar 

  • Skovborg, F., Nielsen, A., Lauritzen, E., andHarktopp, O. (1969): ‘Diameters of the retinal vessels in diabetic and normal subjects’,Diabetes,18, pp. 292–298

    Google Scholar 

  • Soliz, P., Nemeth, S., Swift, M., Edwards, A., Meuer, S., andBerger, J. (2000): ‘Improving the visualisation of drusen in agerelated macular degeneration through maximum entropy digitization and stereo viewing’, Proceedings of SPIE Conference on Medical Imaging 2000: Image Perception and Performance,3981, pp. 217–281

    Google Scholar 

  • Spencer, T., Olson, J., SharP, P., andForrester, J. (1994): ‘A ‘region-growing’ approach to the quantification of microaneurysms in the diabetic retina’. BES/IPSM Meeting Engineering and Technology in Diabetes, Bournemouth, United Kingdom, p. 12

  • Spencer, T., Olson, J., McHardy, K., Sharp, P., andForrester, J. (1996): ‘An image-processing strategy for the segmentation and quantification of microaneurysms in flourescein angiograms of the ocular fundus’,Comput. Biomed. Res. 29, pp. 284–302

    Article  Google Scholar 

  • Tamura, S., Okamoto, Y., andYanashima, K. (1988): ‘Zero-crossing interval correction in tracing eye-fundus blood vessels’,Pattern Recognit.,21, pp. 227–233

    Google Scholar 

  • Tanaka, M., andTanaka, K. (1988): ‘An automatic technique for fundus-photograph mosaic and vascular net reconstruction’: Proceedings of MEDINFO, pp. 116–120

  • Thompson, H. (1999): ‘A wavelet-based method for automated computer detection of diabetic retinopathy in fundus photographs’,Invest. Ophthal. Visual Science,40, p. S121

    Google Scholar 

  • Tolias, Y., andPanas, S. (1998): ‘A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering’,IEEE Trans. Med. Imaging,17, pp. 263–273

    Article  Google Scholar 

  • Wang, Y., andLee, S. (1997): ‘A fast method for automated detection of blood vessels in retinal images’, Asilomar Conference, IEEE Computer Society, Pacific Grove, CA, Vol. 2, pp. 1700–1704

    Google Scholar 

  • Wang, Y., Toonen, H., andMeyer-Ebrecht, D. (1990): ‘A new method of automatic tracking, measuring blood vessels in retinal image’, Annual International Conference of IEEE Engineering in Medicine and Biology Society, Vol. 12, Philadelphia, USA, pp. 174–175

    Google Scholar 

  • Ward, N., Tomlinson, S., andTaylor, C. (1988): ‘Image analysis of fundus photographys’,Ophthalmology,96, pp. 80–86

    Google Scholar 

  • West, G., Brown, J., andEnquist, B. (1997): ‘A general model for the origin of allometric scalling laws in biology’,Science,276, pp. 122–126

    Article  Google Scholar 

  • Wood, S. (1990): ‘Analysis of retinal vessel structure from multiple images’, Annual International Conference of IEEE Engineering in Medicine and Biology Society, Vol. 12, Philadelphia, pp. 176–177

    Google Scholar 

  • Yang, C.-W., Ma, D.-J., Wang, C.-M., Wen, C.-H., Lo, C.-S., andChang, C.-I. (2000): ‘Computer-aided diagnostic detection system of venous beading in retinal images’,Opt. Eng.,39, pp. 1293–1303

    Google Scholar 

  • Ye, D., andZheng, L. (1995): ‘Fundus image processing and feature classification based on mathematical morphology method’, Proceedings of the Canadian Medical and Biological Engineers Conference, Vol. 2, pp. 1015–1016

    Google Scholar 

  • Young, S., George, L., Lusty, J., andOwens, D. (1997): ‘A new screening tool for diabetic retinopathy: the Canon CR5 45NM retinal camera with Frost Medical Software RIS-Lite digital imaging system’,J. Audiovisual Media Med.,20, pp. 11–14

    Google Scholar 

  • Yu, J., Hung, B., andSun, H. (1990): ‘Automatic recognition of retinopathy from retinal images’, Proceedings of Twelfth Annual International Conference on IEEE Engineering and Medicine and Biology Society, Philadelphia, Vol. 12, pp. 171–173

    Google Scholar 

  • Yolong, M., andDingru, X. (1990): ‘Recognizing the glaucoma from ocular fundus image by image processing’, Proceedings of Twelfth Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Philadelphia, Vol. 12, pp. 178–179

    Google Scholar 

  • Zahlmann, G., Schubert, M., Obermaier, M., andMann, G. (1996): ‘Concept of a knowledge based monitoring system for glaucoma and diabetic retinopathy using a telemedicine approach’. 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Amsterdam, pp. 1230–1231

  • Zahlmann, G., Kochner, B., Ugi, I., Schulhmann, D., Liesenfeld, B., Wegner, A., Obermaier, M., andMertz, M. (2000): ‘Hybrid fuzzy image processing for situation assessment: A knowledge-based system for early detection of diabetic retinopathy’,IEEE Eng. Med. Biol.,19, pp. 76–83

    Google Scholar 

  • Zhou, L., Rzeszotarski, M., Singerman, L., andChokreff, J. (1994): ‘The detection and quantification of retinopathy using digital angiograms’,IEEE Trans. Med. Imag.,13, 619–626

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Academic Biomedical Engineering Research Group, School of Design, Engineering & Computing, Bournemouth University, Dorset, UK

    T. Teng, M. Lefley &  D. Claremont

Authors
  1. T. Teng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Lefley
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. D. Claremont
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Claremont.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Teng, T., Lefley, M. & Claremont, D. Progress towards automated diabetic ocular screening: A review of image analysis and intelligent systems for diabetic retinopathy. Med. Biol. Eng. Comput. 40, 2–13 (2002). https://doi.org/10.1007/BF02347689

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02347689

Keywords

Search

Navigation