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Journal of Digital Imaging

, Volume 32, Issue 6, pp 947–962 | Cite as

Artery/Vein Vessel Tree Identification in Near-Infrared Reflectance Retinographies

  • Joaquim de MouraEmail author
  • Jorge Novo
  • José Rouco
  • Pablo Charlón
  • Marcos Ortega
Original Paper

Abstract

An accurate identification of the retinal arteries and veins is a relevant issue in the development of automatic computer-aided diagnosis systems that facilitate the analysis of different relevant diseases that affect the vascular system as diabetes or hypertension, among others. The proposed method offers a complete analysis of the retinal vascular tree structure by its identification and posterior classification into arteries and veins using optical coherence tomography (OCT) scans. These scans include the near-infrared reflectance retinography images, the ones we used in this work, in combination with the corresponding histological sections. The method, firstly, segments the vessel tree and identifies its characteristic points. Then, Global Intensity-Based Features (GIBS) are used to measure the differences in the intensity profiles between arteries and veins. A k-means clustering classifier employs these features to evaluate the potential of artery/vein identification of the proposed method. Finally, a post-processing stage is applied to correct misclassifications using context information and maximize the performance of the classification process. The methodology was validated using an OCT image dataset retrieved from 46 different patients, where 2,392 vessel segments and 97,294 vessel points were manually labeled by an expert clinician. The method achieved satisfactory results, reaching a best accuracy of 93.35% in the identification of arteries and veins, being the first proposal that faces this issue in this image modality.

Keywords

Computer-aided diagnosis Retinal image analysis Vasculature Artery/vein classification Optical coherence tomography 

Notes

Funding Information

This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union through the DTS18/00136 research project and by the Ministerio de Economía y Competitividad, Government of Spain through the DPI2015-69948-R research project. Also, this work has received financial support from the European Union (European Regional Development Fund—ERDF); the Xunta de Galicia, Centro singular de investigación de Galicia accreditation 2016–2019, Ref. ED431G/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047.

Compliance with Ethical Standards

The local ethics committee approved the study and the tenets of the Declaration of Helsinki were followed.

References

  1. 1.
    Albrecht P, Ringelstein M, Müller A, Keser N, Dietlein T, Lappas A, Foerster A, Hartung H, Aktas O, Methner A: Degeneration of retinal layers in multiple sclerosis subtypes quantified by Optical Coherence Tomography. Mult Scler J 18(10): 1422–1429, 2012CrossRefGoogle Scholar
  2. 2.
    Baamonde S, de Moura J, Novo J, Ortega M (2017) Automatic detection of epiretinal membrane in OCT images by means of local luminosity patterns. In: International work-conference on artificial neural networks, pp 222–235Google Scholar
  3. 3.
    Barreira N, Ortega M, Rouco J, Penedo M, Pose-Reino A, Mariño C: Semi-automatic procedure for the computation of the arteriovenous ratio in retinal images. Int J Comput Vis Biomechan 3(2): 135–147, 2010Google Scholar
  4. 4.
    Bellazzi R, Montani S, Riva A, Stefanelli M: Web-based telemedicine systems for home-care: technical issues and experiences. Comput Methods Programs Biomed 64(3): 175–187, 2001PubMedCrossRefGoogle Scholar
  5. 5.
    Biswas S, Lovell BC (2007) Bézier and splines in image processing and machine vision. Science and Business Media:109–121Google Scholar
  6. 6.
    Blanco M, Penedo M, Barreira N, Penas M, Carreira MJ (2006) Localization and extraction of the optic disc using the fuzzy circular hough transform. In: International conference on artificial intelligence and soft computing, pp 712–721Google Scholar
  7. 7.
    de Boor C: A practical guide to splines. Appl Math Sci 27: 1–7, 1978Google Scholar
  8. 8.
    Bowd C, Weinreb RN, Williams JM, Zangwill LM: The retinal nerve fiber layer thickness in ocular hypertensive, normal, and glaucomatous eyes with Optical Coherence Tomography. Arch Ophthalmol 118(1): 22–26, 2000PubMedCrossRefGoogle Scholar
  9. 9.
    Caderno I, Penedo M, Barreira N, Mariño C, Gonzalez F: Precise detection and measurement of the retina vascular tree. Pattern Recogn Image Anal (Adv Math Theory Appl) 15(2): 523–526, 2005Google Scholar
  10. 10.
    Calvo D, Ortega M, Penedo M, Rouco J: Automatic detection and characterisation of retinal vessel tree bifurcations and crossovers in eye fundus images. Comput Methods Programs Biomed 103(1): 28–38, 2011PubMedCrossRefGoogle Scholar
  11. 11.
    Canny J (1986) A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (6):679–698CrossRefGoogle Scholar
  12. 12.
    Dashtbozorg B, Mendonċa AM, Campilho A (2013) Automatic classification of retinal vessels using structural and intensity information. In: Iberian conference on pattern recognition and image analysis, pp 600–607CrossRefGoogle Scholar
  13. 13.
    Diamond E: Manual of diagnostic imaging: a clinician’s guide to clinical problem solving. Radiology 157(1): 18–18, 1985CrossRefGoogle Scholar
  14. 14.
    Dougherty E: Mathematical morphology in image processing New York: CRC Press, 1992Google Scholar
  15. 15.
    Earley M: Clinical anatomy of the eye. Optom Vis Sci 77(5): 231–232, 2000CrossRefGoogle Scholar
  16. 16.
    Fercher AF, Drexler W, Hitzenberger CK, Lasser T: Optical Coherence Tomography-principles and applications. Rep Progress Phys 66(2): 239, 2003CrossRefGoogle Scholar
  17. 17.
    Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. In: International conference on medical image computing and computer-assisted intervention, pp 130–137CrossRefGoogle Scholar
  18. 18.
    Gómes E, Del Pozo F, Quiles J, Arredondo M, Rahms H, Sanz M, Cano P, et al.: A telemedicine system for remote cooperative medical imaging diagnosis. Comput Methods Programs Biomed 49(1): 37–48, 1996CrossRefGoogle Scholar
  19. 19.
    González-López A, Ortega M, Penedo M, Charlón P (2014) Automatic robust segmentation of retinal layers in OCT images with refinement stages. In: International conference image analysis and recognition, pp 337–345Google Scholar
  20. 20.
    Grisan E, Ruggeri A (2003) A divide et impera strategy for automatic classification of retinal vessels into arteries and veins. In: Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th annual international conference of the IEEE, vol 1, pp 890–893Google Scholar
  21. 21.
    Ho A: Retina: Color Atlas & Synopsis of Clinical Ophthalmology (Wills Eye Hospital Series) New York: McGraw-Hill Professional, 2003Google Scholar
  22. 22.
    Huang T, Yang G, Tang G: A fast two-dimensional median filtering algorithm. IEEE Trans Acoust Speech Signal Process 27(1): 13–18, 1979CrossRefGoogle Scholar
  23. 23.
    Hubbard LD, Brothers RJ, King WN, Clegg LX, Klein R, Cooper LS, Sharrett AR, Davis MD, Cai J: Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the atherosclerosis risk in communities study. Ophthalmology 106(12): 2269–2280, 1999PubMedCrossRefGoogle Scholar
  24. 24.
    Ikram M, De Jong F, Bos M, Vingerling J, Hofman A, Koudstaal PJ, De Jong P, Breteler M: Retinal vessel diameters and risk of stroke the rotterdam study. Neurology 66(9): 1339–1343, 2006PubMedCrossRefGoogle Scholar
  25. 25.
    Jonas JB, Schmidt AM, Müller-Bergh J, Schlötzer-Schrehardt U, Naumann G: Human optic nerve fiber count and optic disc size. Invest Ophthalmol Vis Sci 33(6): 2012–2018, 1992PubMedGoogle Scholar
  26. 26.
    Joshi VS, Reinhardt JM, Garvin MK, Abramoff MD: Automated method for identification and artery-venous classification of vessel trees in retinal vessel networks. PloS One 9(2): e88,061, 2014CrossRefGoogle Scholar
  27. 27.
    Kass M, Witkin A, Terzopoulos D (1987) Snakes: Active contour models. In: 1St international conference on computer vision, vol 259, pp 268Google Scholar
  28. 28.
    Kondermann C, Kondermann D, Yan M, et al. (2007) Blood vessel classification into arteries and veins in retinal images. In: Proceedings of SPIE Medical Imaging, pp 651,247–6512,479Google Scholar
  29. 29.
    López AM, Lloret D, Serrat J, Villanueva JJ: Multilocal creaseness based on the level-set extrinsic curvature. Comput Vis Image Underst 77(2): 111–144, 2000CrossRefGoogle Scholar
  30. 30.
    MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability, vol 1, pp 281–297Google Scholar
  31. 31.
    de Moura J, Novo J, Charlón P, Barreira N, Ortega M: Enhanced visualization of the retinal vasculature using depth information in OCT. Med Biol Eng Comput 55(12): 2209–2225, 2017PubMedCrossRefGoogle Scholar
  32. 32.
    de Moura J, Novo J, Rouco J, Penedo M, Ortega M (2017) Automatic identification of intraretinal cystoid regions in Optical Coherence Tomography. In: Conference on artificial intelligence in medicine in Europe, pp 305–315Google Scholar
  33. 33.
    Novo J, Penedo M, Santos J (2008) Optic disc segmentation by means of GA-optimized Topological Active Nets. In: International conference image analysis and recognition, pp 807–816Google Scholar
  34. 34.
    Ortega M, Barreira N, Novo J, Penedo M, Pose-Reino A, Gómez-Ulla F: Sirius: a web-based system for retinal image analysis. Int J Med Inf 79(10): 722–732, 2010CrossRefGoogle Scholar
  35. 35.
    Philip KP, Dove EL, McPherson DD, Gotteiner NL, Stanford W, Chandran KB: The fuzzy hough transform-feature extraction in medical images. IEEE Trans Med Imaging 13(2): 235–240, 1994PubMedCrossRefGoogle Scholar
  36. 36.
    Puzyeyeva O, Lam WC, Flanagan JG, Brent MH, Devenyi RG, Mandelcorn MS, Wong T, Hudson C: High-resolution Optical Coherence Tomography retinal imaging: a case series illustrating potential and limitations. J Ophthalmol 2011: 1–6, 2011CrossRefGoogle Scholar
  37. 37.
    Relan D, MacGillivray T, Ballerini L, Trucco E (2013) Retinal vessel classification: sorting arteries and veins. In: Engineering in medicine and biology society, 2013 35th annual international conference of the IEEE, pp 7396–7399Google Scholar
  38. 38.
    Relan D, MacGillivray T, Ballerini L, Trucco E (2014) Automatic retinal vessel classification using a least square-support vector machine in vampire. In: 2014 36th annual international conference of the IEEE Engineering in medicine and biology society, pp 142–145Google Scholar
  39. 39.
    Rothaus K, Jiang X, Rhiem P: Separation of the retinal vascular graph in arteries and veins based upon structural knowledge. Image Vis Comput 27(7): 864–875, 2009CrossRefGoogle Scholar
  40. 40.
    Samagaio G, Estévez A, de Moura J, Novo J, Fernandez MI: Ortega, m.: automatic macular edema identification and characterization using OCT images. Comput Methods Programs Biomed 21: 327–335, 2018Google Scholar
  41. 41.
    Sánchez L, Barreira N, Penedo M, de Tuero GC (2014) Computer aided diagnosis system for retinal analysis: automatic assessment of the vascular tortuosity. In: Studies in health technology and informatics: Innovation in medicine and healthcare, pp 55–64Google Scholar
  42. 42.
    Sánchez-Tocino H., Alvarez-Vidal A, Maldonado MJ, Moreno-Montaṅés J, Garcia-Layana A: Retinal thickness study with Optical Coherence Tomography in patients with diabetes. Invest Ophthalmol Vis Sci 43(5): 1588–1594, 2002PubMedGoogle Scholar
  43. 43.
    Schmitt JM: Optical Coherence Tomography (OCT): a review. IEEE J Sel Top Quantum Electron 5(4): 1205–1215, 1999CrossRefGoogle Scholar
  44. 44.
    Simó A, de Ves E: Segmentation of macular fluorescein angiographies. A statistical approach. Pattern Recogn 34(4): 795–809, 2001CrossRefGoogle Scholar
  45. 45.
    Sinthanayothin C, Boyce JF, Cook HL, Williamson TH: Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. Br J Ophthalmol 83(8): 902–910, 1999PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    Vázquez S, Cancela B, Barreira N, Penedo M, Rodríguez-blanco M, Seijo MP, de Tuero GC, Barceló MA, Saez M: Improving retinal artery and vein classification by means of a minimal path approach. Mach Vis Appl 24(5): 919–930, 2013CrossRefGoogle Scholar
  47. 47.
    Williams ZY, Schuman JS, Gamell L, Nemi A, Hertzmark E, Fujimoto JG, Mattox C, Simpson J, Wollstein G: Optical Coherence Tomography measurement of nerve fiber layer thickness and the likelihood of a visual field defect. Amer J Ophthalmol 134(4): 538–546, 2002PubMedCrossRefGoogle Scholar
  48. 48.
    Wong TY, Klein R, Sharrett AR, Schmidt MI, Pankow JS, Couper DJ, Klein BE, Hubbard LD, Duncan BB: Retinal arteriolar narrowing and risk of diabetes mellitus in middle-aged persons. J Amer Med Assoc 287(19): 2528–2533, 2002CrossRefGoogle Scholar
  49. 49.
    Xu X, Ding W, Abràmoff MD, Cao R: An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image. Comput Methods Programs Biomed 141: 3–9, 2017PubMedCrossRefGoogle Scholar
  50. 50.
    Yang Y, Bu W, Wang K, Zheng Y, Wu X (2016) Automated artery-vein classification in fundus color images. In: International conference of young computer scientists, engineers and educators, pp 228–237Google Scholar
  51. 51.
    Yu S, Wei Z, Deng RH, Yao H, Zhao Z, Ngoh LH, Wu Y (2008) A tele-ophthalmology system based on secure video-conferencing and white-board. In: 2008. Healthcom 2008. 10th international conference E-health networking, applications and services, pp 51–52Google Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of A CoruñaA CoruñaSpain
  2. 2.CITIC - Research Center of Information and Communication TechnologiesUniversity of A CoruñaA CoruñaSpain
  3. 3.Instituto Oftalmológico Victoria de RojasA CoruñaSpain

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