Skip to main content

Automated Image Analysis and the Application of Diagnostic Algorithms in an Ocular Telehealth Network

  • Chapter
  • First Online:
Book cover Digital Teleretinal Screening

Abstract

The application of computer-based imaging algorithms to the diagnosis of human disease is already a reality, used routinely today in radiology, mammography, and pathology [1–3]. Recent advances in the imaging of the eye, in particular nonmydriatic and cross-sectional images of the retina, now provide high-quality digital data to diagnose and quantify features of many diseases, including diabetic retinopathy (DR). The potential of these imaging methods is clear. New computer-based systems and diagnostic algorithms hold the promise of producing low-cost, potentially automated, diagnostic imaging systems for managing diseases like DR on a societal scale.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bartels PH, Wied GL (1981) Automated image ­analysis in clinical pathology. Am J Clin Pathol 75(3 Suppl):489–493

    PubMed  CAS  Google Scholar 

  2. Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ (1996) Automated analysis of mammographic densities. Phys Med Biol 41:909–923

    Article  PubMed  CAS  Google Scholar 

  3. Katsuragawa S, Doi K (2007) Computer-aided diagnosis in chest radiography. Comput Med Imaging Graph 31(4–5):212–223

    Article  PubMed  Google Scholar 

  4. Prevention of blindness from diabetes mellitus: report of a WHO consultation. World Health Organization. Available at http://www.who.int/diabetes/en. Accessed on 2006

  5. International clinical classification of diabetic retinopathy, severity of diabetic macular edema. Inter-national Council of Ophthalmology. Available at http://www.icoph.org/enhancing_eyecare/international_clinical_guidelines.html. Accessed on 2011

  6. Pugh JA, Jacobson JM, Van Heuven WA, Watters JA, Tuley MR, Lairson DR, Lorimor RJ, Kapadia AS, Velez R (1993) Screening for diabetic retinopathy. The wide-angle retinal camera. Diabetes Care 16(6):889–895

    Article  PubMed  CAS  Google Scholar 

  7. Javitt JC, Aiello LP, Bassi LJ, Chiang YP, Canner JK (1991) Detecting and treating retinopathy in patients with type I diabetes mellitus: savings associated with improved implementation of current guidelines. Ophthalmology 98(10):1565–1573

    PubMed  CAS  Google Scholar 

  8. Harris MI, Flegal KM, Cowie CC, Eberhardt MS, Goldstein DE, Little RR, Wiedmeyer HM, Byrd-Holt DD (1998) Prevalence of diabetes, impaired fasting glucose, and impaired glucose tolerance in U.S. Adults. The third national health and nutrition examination survey, 1988–1994. Diabetes Care 21(4):518–524

    Article  PubMed  CAS  Google Scholar 

  9. Lin DY, Blumenkranz MS, Brothers RJ, Grosvenor DM (2002) The sensitivity and specificity of single-field nonmydriatic monochromatic digital fundus photography with remote image interpretation for diabetic retinopathy screening: a comparison with ophthalmoscopy and standardized mydriatic color photography. Am J Ophthalmol 134:204–213

    Article  PubMed  Google Scholar 

  10. Rudnisky CJ, Tennant MTS, Weis E, Ting A, Hinz BJ, Greve MDJ (2007) Web-based grading of compressed stereoscopic digital photography versus standard slide film photography for the diagnosis of diabetic retinopathy. Ophthalmology 114:1748–1754

    Article  PubMed  Google Scholar 

  11. Wei J, Valentino D, Bell D, Baker R (2006) A web-based telemedicine system for diabetic retinopathy screening using digital fundus photography. Telemed J E Health 12:50–57

    Article  PubMed  Google Scholar 

  12. Cavallerano AA, Cavallerano JD, Katalinic P, Tolson AM, Aiello LP, Aiello LM (2003) Joslin Vision Network Clinical Team. Use of Joslin Vision Network digital-video nonmydriatic retinal imaging to assess diabetic retinopathy in a clinical program. Retina 23:215–223

    Article  PubMed  Google Scholar 

  13. Li Y, Karnowski TP, Tobin KW, Giancardo L, Morris S, Sparrow SE, Garg S, Fox K, Chaum E (2011). A health insurance portability and accountability act-compliant ocular telehealth network for the remote diagnosis and management of diabetic retinopathy. Telemed J E Health 17(8):627–634

    Google Scholar 

  14. Teng T, Lefley M, Claremont D (2001) Progress towards automated diabetic ocular screening: a review of image analysis and intelligent systems for diabetic retinopathy. Med Biol Eng Comput 40:2–13

    Article  Google Scholar 

  15. Fleming AD, Philip S, Goatman KA, Olson JA, Sharp PF (2006) Automated assessment of diabetic retinal image quality based on clarity and field definition. Invest Ophthalmol Vis Sci 47(3):120–1125

    Article  Google Scholar 

  16. Niemeijer M, Abramoff MD, van Ginneken B (2006) Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening. Med Image Anal 10(6):888–898

    Article  PubMed  Google Scholar 

  17. Usher DB, Himaga M, Dumskyj MJ (2003) Automated assessment of digital fundus image quality using detected vessel area. In: Proceeding of medical image understanding and analysis, British Machine Vision Association (BMVA), Sheffield, 2003, pp 81–84

    Google Scholar 

  18. Lalonde M, Beaulieu M, Gagnon L (2001) Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching. IEEE Trans Med Imaging 20(11):1193–1200

    Google Scholar 

  19. Giancardo L, Abramoff MD, Chaum E, Karnowski TP, Meriaudeau F, Tobin KW (2008) Elliptical local vessel density: a fast and robust quality metric for ­fundus images. Conf Proc IEEE Eng Med Biol Soc 35:34–37

    Google Scholar 

  20. Giancardo L, Meriaudeau F, Karnowski TP, Chaum E and Tobin K (2010) Quality assessment of retinal fundus images using elliptical local vessel density. In New Developments in Biomedical Engineering, Ed. Domenico Campolo, INTECH. http://sciyo.com/books/show/title/new-developments-in-biomedical-engineering

    Google Scholar 

  21. 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  PubMed  CAS  Google Scholar 

  22. Hoover A, Kouznetsova V, Goldbaum M (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging 19(3):203–210

    Article  PubMed  CAS  Google Scholar 

  23. 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(1):131–137

    Article  Google Scholar 

  24. Chapman N, Witt N, Gao X, Bharath AA, Stanton AV, Thom SA, Hughes AD (2001) Computer algorithms for the automated measurement of retinal arteriolar diameters. Br J Ophthalmol 85(1):74–79

    Google Scholar 

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

    Article  PubMed  Google Scholar 

  26. Niemeijer M, Staal J, van Ginneken B, Loog M, Abramoff MD (2004) Comparative study of retinal vessel segmentation methods on a new publicly available database. Proc SPIE v5370:648–656

    Article  Google Scholar 

  27. Fang B, Hsu W, Lee ML (2003) Reconstruction of vascular structures in retinal images. In: Proceedings of the international conference on image processing (ICIP’03), vol. II, Barcelona, 2003, pp 157–160

    Google Scholar 

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

    Article  CAS  Google Scholar 

  29. Foracchia M, Grisan E, Ruggeri A (2004) Detection of the optic disk in retinal images by means of a geometrical model of vessel structure. IEEE Trans Med Imaging 23:1189–1195

    Article  PubMed  CAS  Google Scholar 

  30. 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 22:951–958

    Article  PubMed  Google Scholar 

  31. Niemeijer M, Abramoff MD, van Ginneken B (2007) Segmentation of the optic disc, macula and vascular arch in fundus photographs. IEEE Trans Med Imaging 26(1):116–127

    Article  PubMed  Google Scholar 

  32. Niemeijer M, Abramoff MD, van Ginneken B (2009) Fast detection of the optic disc and fovea in color fundus photographs. Med Image Anal 13:859–870

    Article  PubMed  Google Scholar 

  33. Youssif A-R, Ghalwash AZ, Ghoneim A-R (2008) Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter. IEEE Trans Med Imaging 27(1):11–18

    Article  PubMed  CAS  Google Scholar 

  34. Karnowski TP, Aykac D, Chaum E, Giancardo L, Li Y, Tobin KW Jr, Abramoff MD (2009) Practical considerations for optic nerve location in telemedicine. Conf Proc IEEE Eng Med Biol Soc 1:6205–6209

    Google Scholar 

  35. Tobin KW, Chaum E, Govindasamy VP, Karnowski TP (2007) Detection of anatomic structures in human retinal imagery. IEEE Trans Med Imaging 26:1729–1739

    Article  PubMed  Google Scholar 

  36. 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 

  37. Karnowski TP, Govindasamy VP, Tobin KW, Chaum E (2006) Locating the optic nerve in retinal images: comparing model-based and Bayesian decision methods. Conf Proc IEEE Eng Med Biol Soc 1:4436–4439

    Article  PubMed  Google Scholar 

  38. Karnowski TP, Aykac D, Chaum E, Giancardo L, Li Y, Tobin KW, Abràmoff MD (2009) Evaluating the accuracy of optic nerve detections in retina imaging by using complementary methods. In: Proceedings of world congress of medical physics and biomedical engineering, Munich, 2009

    Google Scholar 

  39. Marquardt D (1963) An algorithm for least-squares estimation of nonlinear parameters. SIAM J Appl Math 11:431–441

    Article  Google Scholar 

  40. Patton N, Aslam TM, MacGillivray T, Deary IJ, Dhillon B, Eikelboom RH, Yogesan K, Constable IJ (2006) Retinal image analysis: concepts, applications and potential. Prog Retin Eye Res 25:99–127

    Article  PubMed  Google Scholar 

  41. Niemeijer M, van Ginneken B, Cree MJ, Mizutani A 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 

  42. Giancardo L, Meriaudeau F, Karnowski TP, Chaum E, Tobin KW, Li Y (2010) Microaneurysms detection with the Radon Cliff Operator in retinal fundus images. In: Proceedings of SPIE medical imaging, San Diego, 2010

    Google Scholar 

  43. Giancardo L, Chaum E, Karnowski TP, Meriaudeau F, Tobin KW, Li Y (2009) Bright retinal lesions detection using color fundus images containing reflective features. In: Proceedings of world congress of medical physics and biomedical engineering, Munich, 2009

    Google Scholar 

  44. Chaum E, Karnowski TP, Govindasamy VP, Abdel­rahamen M, Tobin KW (2008) Automated diagnosis of retinopathy by content-based image retrieval. Retina 28:1463–1477

    Article  PubMed  Google Scholar 

  45. Tobin KW, Abramoff MD, Chaum E, Giancardo L, Govindasamy VP, Karnowski TP, Tennant MTS (2008) Using a patient image archive to diagnose retinopathy. Conf Proc IEEE Eng Med Biol Soc 1:5441–5444

    Google Scholar 

  46. Tobin KW, Abdelrahman M, Chaum E, Govindasamy VP, Karnowski TP (2007) A probabilistic framework for content-based diagnosis of retinal disease. Conf Proc IEEE Eng Med Biol Soc 2007:6744–6747

    PubMed  Google Scholar 

  47. Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York

    Google Scholar 

  48. Abramoff MA, Suttorp-Schulten M (2005) Web-based screening for diabetic retinopathy in a primary care population: the eyecheck project. Telemed J E Health 11:668–674

    Article  PubMed  Google Scholar 

  49. Abramoff MA, Veirgever M, Neimeijer M, Russel S, Suttorp-Schulten M, 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 31:193–198

    Article  PubMed  Google Scholar 

  50. Niemeijer M, van Ginneken B, Staal J, Suttorp-Schulten MS, Abramoff MD (2005) Automatic ­detection of red lesions in digital color fundus photographs. IEEE Trans Med Imaging 24:584–592

    Article  PubMed  Google Scholar 

  51. Niemeijer M, Abramoff MD, van Ginneken B (2009) Information fusion for diabetic retinopathy CAD in digital color fundus photographs. IEEE Trans Med Imaging v28:775–785

    Article  Google Scholar 

Download references

Acknowledgments

These studies were supported in part by grants from Oak Ridge National Laboratory, the National Eye Institute, (EY017065), and the Health Resources and Services Administration, by an unrestricted UTHSC departmental grant from Research to Prevent Blindness, New York, NY and by the Plough Foundation, Memphis, TN.

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Karnowski, T.P., Li, Y., Giancardo, L., Aykac, D., Tobin, K.W., Chaum, E. (2012). Automated Image Analysis and the Application of Diagnostic Algorithms in an Ocular Telehealth Network. In: Yogesan, K., Goldschmidt, L., Cuadros, J. (eds) Digital Teleretinal Screening. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25810-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-25810-7_5

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25809-1

  • Online ISBN: 978-3-642-25810-7

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics