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Detection of Anatomic Structures in Retinal Images

  • José Pinão
  • Carlos Manta Oliveira
  • André Mora
  • João Dias
Chapter
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 8)

Abstract

A retinal image presents three important structures in a healthy eye: optic disk, fovea and blood vessels. These are diseases associated with changes in each of these structures. Some parameters should be extracted in order to evaluate if an eye is healthy. For example, the level of imperfection of the optic disk’s circle contour is related with glaucoma. Furthermore, the proximity of the lesion in the retina to the fovea (structure responsible for the central vision) induces loss of vision. Advanced stages of diabetic retinopathy cause the formation of micro blood vessels that increase the risk of detachment of the retina or prevent light from reaching the fovea. On the other hand, the arterio-venous ratio calculated through the thickness of the central vein and artery of the retina, is a parameter extracted from the vessels segmentation. In image processing, each structure detected has special importance to detect the others, since each one can be used as a landmark to the others. Moreover, often masking the optic disk is crucial to reach good results with algorithms to detect other structures. The performance of the detection algorithms is highly related with the quality of the image and with the existence of lesions. These issues are discussed below.

Keywords

Optic Disk Retinal Image Image Quality Assessment Active Shape Model Illumination Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Jelinek HF, Cree MJ (2009) Automated image detection of retinal pathology. CRC Press, Boca RatonCrossRefGoogle Scholar
  2. 2.
    Davis H, Russell S, Barriga E, Abràmoff MD, Soliz P (2009) Vision-based, real-time retinal image quality assessment. Russell J Bertrand Russell Archives, pp 1–6Google Scholar
  3. 3.
    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 47:1120–1125CrossRefGoogle Scholar
  4. 4.
    Boucher MCC, Gresset JA, Angioi K, Olivier S (2003) Effectiveness and safety of screening for diabetic retinopathy with two nonmydriatic digital images compared with the seven standard stereoscopic photographic fields. Can J Ophthalmol. J canadien d’ophtalmologie. 38:557–568Google Scholar
  5. 5.
    Olson JA, Sharp PF, Fleming AD, Philip S (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:e63CrossRefGoogle Scholar
  6. 6.
    Zimmer-Galler I, Zeimer R (2006) Results of implementation of the DigiScope for diabetic retinopathy assessment in the primary care environment. Telemed J e-health Off J Am Telemed Assoc 12:89–98CrossRefGoogle Scholar
  7. 7.
    Philip S, Cowie LM, Olson JA (2005) The impact of the health technology board for Scotland’s grading model on referrals to ophthalmology services. Br J ophthalmol 89:891–896CrossRefGoogle Scholar
  8. 8.
    Heaven CJ, Cansfield J, Shaw KM (1993) The quality of photographs produced by the non-mydriatic fundus camera in a screening programme for diabetic retinopathy: a 1 year prospective study. Eye London England 7(Pt 6):787–790CrossRefGoogle Scholar
  9. 9.
    Abràmoff MD, Suttorp-Schulten MSA (2005) Web-based screening for diabetic retinopathy in a primary care population: the EyeCheck project. Telemed J ehealth Off J Am Telemed Assoc 11:668–674CrossRefGoogle Scholar
  10. 10.
    Department of Ophthalmology and Visual Sciences of the University of Wisconsin-Madison, F.P.R.C.: ARIC Grading Protocol. http://eyephoto.ophth.wisc.edu/researchareas/hypertension/lbox/LTBXPROT_995.html
  11. 11.
    Lee SC, Wang Y (1999) Automatic retinal image quality assessment and enhancement. In: Proceedings of SPIE, p 1581Google Scholar
  12. 12.
    Lalonde M, Gagnon L, Boucher MCC (2001) Automatic visual quality assessment in optical fundus images. In: Proceedings of Vision Interface 2001, pp 259–264Google Scholar
  13. 13.
    Bartling H, Wanger P, Martin L (2009) Automated quality evaluation of digital fundus photographs. Acta Ophthalmol 87:643–647CrossRefGoogle Scholar
  14. 14.
    Acharya T, Ray AK (2005) Image processing: principles and applications. Wiley, HobokenGoogle Scholar
  15. 15.
    Hunter A, Lowell JA, Habib M, Ryder B, Basu A, Steel D (2011) An automated retinal image quality grading algorithm. In: Proceedings of the annual international conference of the IEEE engineering in medicine and biology society conference, pp 5955–5958Google Scholar
  16. 16.
    Nirmala SR, Dandapat S, Bora PK (2011) Performance evaluation of distortion measures for retinal images. Int J Comput Appl 17:17Google Scholar
  17. 17.
    Niemeijer M, Abràmoff MD, van Ginneken B (2006) Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening. Med Image Anal 10:888–898CrossRefGoogle Scholar
  18. 18.
    Giancardo L, Abràmoff MD, Chaum E, Karnowski TP, Meriaudeau F, Tobin KW (2008) Elliptical local vessel density: a fast and robust quality metric for retinal images. In: Proceedings of the international conference on IEEE engineering in medicine and biology society, pp 3534–3537Google Scholar
  19. 19.
    Paulus J, Meier J, Bock R, Hornegger J, Michelson G (2010) Automated quality assessment of retinal fundus photos. Int J Comput Assist Radiol Surg 5:557–564CrossRefGoogle Scholar
  20. 20.
    Smith RT, Nagasaki T, Sparrow JR, Barbazetto I, Klaver CC, Chan JK (2003) A method of drusen measurement based on the geometry of fundus reflectance. Biomed Eng Online 2:10CrossRefGoogle Scholar
  21. 21.
    Soliz P, Wilson MP, Nemeth SC, Nguyen P (2002) Computer-aided methods for quantitative assessment of longitudinal changes in retinal images presenting with maculopathy. In: Medical Imaging 2002: visualization, image-guided procedures, and display, SPIE, San Diego, pp 159–170Google Scholar
  22. 22.
    Phillips RP, Spencer T, Ross PG, Sharp PF, Forrester JV (1991) Quantification of diabetic maculopathy by digital imaging of the fundus. Eye 5(Pt 1):130–137CrossRefGoogle Scholar
  23. 23.
    Jagoe JR, Blauth CI, Smith PL, Arnold JV, Taylor KM, Wootton R (1990) Quantification of retinal damage during cardiopulmonary bypass: comparison of computer and human assessment. In: Proceedings of the IEE communications, speech and vision I(137):170–175CrossRefGoogle Scholar
  24. 24.
    Rapantzikos K, Zervakis M, Balas K (2003) Detection and segmentation of drusen deposits on human retina: potential in the diagnosis of age-related macular degeneration. Med Image Anal 7:95–108CrossRefGoogle Scholar
  25. 25.
    Gonzalez R, Woods R (1993) Digital image processing. Addison Wesley Publishing, New YorkGoogle Scholar
  26. 26.
    Mora AD, Vieira PM, Manivannan A, Fonseca JM (2011) Automated drusen detection in retinal images using analytical modelling algorithms. Biomed Eng Online 10:59Google Scholar
  27. 27.
    Culpin D (1986) Calculation of cubic smoothing splines for equally spaced data. Numer Math 48:627–638MathSciNetzbMATHCrossRefGoogle Scholar
  28. 28.
    Smith RT, Chan JK, Nagasaki T, Ahmad UF, Barbazetto I, Sparrow J, Figueroa M, Merriam J (2005) Automated detection of macular drusen using geometric background leveling and threshold selection. Arch Ophthalmol 123:200–206CrossRefGoogle Scholar
  29. 29.
    Shlens J (2005) A tutorial on principal component analysis. Measurement 51:52Google Scholar
  30. 30.
    Newsom RS, Sinthanayothin C, Boyce J, Casswell AG, Williamson TH (2000) Clinical evaluation of “local contrast enhancement” for oral fluorescein angiograms. Eye (London, England) 14 (Pt 3A):318–23Google Scholar
  31. 31.
    Jobson DJ, Rahman Z, Woodell GA (1997) A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans Image Process Publ IEEE Signal Process Soc 6:965–976CrossRefGoogle Scholar
  32. 32.
    Majumdar J, Nandi M, Nagabhushan P (2011) Retinex algorithm with reduced halo artifacts. Def Sci J 61:559–566Google Scholar
  33. 33.
    Land EH, McCann JJ (1971) Lightness and retinex theory. J Opt Soc Am 61:1–11CrossRefGoogle Scholar
  34. 34.
    Foracchia M, Grisan E, Ruggeri A, Member S (2004) Detection of optic disc in retinal images by means of a geometrical model of vessel structure. IEEE Trans Med Imaging 2004(23):1189–1195CrossRefGoogle Scholar
  35. 35.
    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 22:951–958CrossRefGoogle Scholar
  36. 36.
    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 20:1193–1200CrossRefGoogle Scholar
  37. 37.
    Youssif AR, Ghalwash AZ, Ghoneim AR (2008) Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter. IEEE Trans Med Imaging 27:11–18Google Scholar
  38. 38.
    Mendels F, Heneghan C, Thiran JP (1999) Identification of the optic disk boundary in retinal images using active contours. In: Proceedings of the Irish machine vision and image processing conference. Citeseer, pp 103–115Google Scholar
  39. 39.
    Osareh A, Mirmehdi M, Thomas B, Markham R (2002) Colour morphology and snakes for optic disc localisation. In: Proceedings of the 6th medical image understanding and analysis conference, pp 21–24Google Scholar
  40. 40.
    Sinthanayothin C, Boyce JF, Cook HL, Williamson TH (1999) Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. Br J Ophthalmol 83:902–910CrossRefGoogle Scholar
  41. 41.
    Li H (2003) Boundary detection of optic disk by a modified ASM method. Pattern Recogn 36:2093–2104zbMATHCrossRefGoogle Scholar
  42. 42.
    Kavitha D, Devi SS (2005) Automatic detection of optic disc and exudates in retinal images. In: Proceedings of 2005 international conference on intelligent sensing and information processing, pp 501–506Google Scholar
  43. 43.
    Sekhar S, Al-Nuaimy W, Nandi A (2008) Automated localisation of retinal optic disk using hough transform. In: Proceedings of the 5th IEEE international symposium on biomedical imaging from Nano to Macro. ISBI 2008, pp 1577–1580Google Scholar
  44. 44.
    Zhu X, Rangayyan RM (2008) Detection of the optic disc in images of the retina using the Hough transform. In: Proceedings of the Annual International Conference on the IEEE engineering in medicine and biology society, pp 3546–3549Google Scholar
  45. 45.
    Cootes T (1995) Active shape models-their training and application. Comput Vis Image Underst 61:38–59CrossRefGoogle Scholar
  46. 46.
    Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66CrossRefGoogle Scholar
  47. 47.
    Gonzalez R, Woods R (2002) Digital image processing. Prentice Hall, Upper Saddle RiverGoogle Scholar
  48. 48.
    Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell PAMI-8:679–698Google Scholar
  49. 49.
    Pinão J, Oliveira CM (2011) Fovea and optic disc detection in retinal images. In: Tavares JM, Natal Jorge RS (eds) Computational vision and medical image processing VIPIMAGE 2011. CRC Press, pp 149–153Google Scholar
  50. 50.
    ter Haar F (2005) Automatic localization of the optic disc in digital colour images of the human retina. Utrecht University, The NetherlandsGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • José Pinão
    • 1
  • Carlos Manta Oliveira
    • 1
  • André Mora
    • 2
  • João Dias
    • 3
  1. 1.Critical HealthCoimbraPortugal
  2. 2.Universidade Nova de LisboaLisbonPortugal
  3. 3.Universidade de CoimbraCoimbraPortugal

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