Methods for digital analysis of human vascular system. Literature review
N.Yu. Ilyasova

PDF, 1759 kB

Full text of article: Russian language.

DOI: 10.18287/0134-2452-2013-37-4-511-535

Pages: 511-535.

Abstract:
A review of key approaches to the digital analysis of the human vascular system images is given. We outline major stages of diagnostic image processing and analyze different approaches to the extraction and quantification of blood vessel morphological features.

Key words:
human vascular system, image processing.

References:

  1. Teng, T. Progress towards automated diabetic ocular screening: a review of image analysis and intelligent systems for diabetic retinopathy / T. Teng, D. Claremont, M. Lefley // Med. Biol. Eng. Comput. – 2002. – Vol. 40(1). – P. 2-13.
  2. Heneghan, C. Characterization of changes in blood vessel width and tortuosity in retinopathy of prematurity using image analysis / C. Heneghan, J. Flynn, M. O'Keefe, M. Cahill // Med. Image. Anal. – 2002. – Vol. 6(4). – P. 407-29.
  3. Cheung, C.S. Computer-assisted image analysis of temporal retinal vessel width and tortuosity in retinopathy of prematurity for the assessment of disease severity and treatment outcome / C.S. Cheung, Z. Butty, N.N. Tehrani, W.C. Lam // J AAPOS. – 2011. – Vol. 15(4). – P. 374-380.
  4. Haddouche, A. Detection of the foveal avascular zone on retinal angiograms using Markov random fields / A. Haddouche, M. Adel, M. Rasigni, J. Conrath, S. Bourennane // Digital Signal Processing. – 2010. – Vol. 20(1). – P. 149-154.
  5. Grisan, E. A divide et impera strategy for automatic classification of retinal vessels into arteries and veins / E. Grisan, A. Ruggeri // Engineering in Medicine and Biology Society 2003 / Proceedings of the 25th Annual International Conference of the IEEE. – 2003. – Vol. 891. – P. 890-893.
  6. Lowell, J. Measurement of retinal vessel widths from fundus images based on 2-D Modeling / J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, R.L. Kennedy // IEEE Trans Med Imaging. – 2004. – Vol 3(10). – P. 1196-1204.
  7. Foracchia, M. Extraction and quantitative description of vessel features in hypertensive retinopathy fundus images / M. Foracchia // Book Abstracts / 2nd International Workshop on Computer Assisted Fundus Image Analysis. – 2001. – P. 6.
  8. Kose, C. A personal identification system using retinal vasculature in retinal fundus images / C. Kose, C. Ikibas // Expert Systems with Applications. – 2011. – Vol. 38(11). – P. 13670-13681.
  9. Fraz, M.M. Blood vessel segmentation methodologies in retinal images / M.M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A.R. Rudnicka, C.G. Owen, S.A. Barman // Comput Methods Programs Biomed. – 2012. – Vol. 108(1). – P. 407-433.
  10. Winder, R.J. Algorithms for digital image processing in diabetic retinopathy / R.J. Winder, P.J. Morrow, I.N. McRitchie, J.R. Bailie, P.M. Hart // Computerized Medical Imaging and Graphics. – 2009. – Vol. 33(8). – P. 608-622.
  11. Felkel, P. Vessel tracking in peripheral CTA datasets – an overview / P. Felkel, R. Wegenkittl, A. Kanitsar. – Computer Graphics (Spring Conference on), 2001. – P. 232-239.
  12. Buhler, K. Geometric methods for vessel visualization and quantification – a Survay / K. Buhler, P. Felkel, A.L. Cruz // Geometric Modelling for Scientific Visualization. – 2003. – P. 399-421.
  13. Kirbas, C. A review of vessel extraction techniques and algorithms / C. Kirbas, F. Quek // ACM Computing. – 2004. – Vol. 36(2). – P. 81-121.
  14. Mabrouk, M.S. Survey of retinal image segmentation and registration / M.S. Mabrouk, N.H. Solouma, Y.M. Kadah // GVIP Journal. – 2006. – Vol. 6(2). – P. 1-11.
  15. Faust, O.R.A.U. Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review / O.R.A.U. Faust, E.Y.K. Ng, K.-H. Ng, J.S. Suri // Journal of Medical Systems. – 2012. – Vol. 36(1). – P. 145-57.
  16. Akita, K. A computer method of understanding ocular fundus images / K. Akita, H. Kuga // Pattern Recognition. – 1982. – Vol. 15. – P. 431-443.
  17. Thackray, B.D. Semi-automatic segmentation of vascular network images using a rotating structuring element (rose) with mathematical morphology and dual feature thresholding / B.D. Thackray, A.C. Nelson // IEEE Trans. Med. Imaging. – 1993. – Vol. 12(3). – P. 385-392.
  18. American Academy of Ophthalmology, Ophthalmic Pathology // in Basic and Clinical Science Courses. – section 11, 179,1991.
  19. Staal, J.J. Ridge based vessel segmentation in color images of the retina / J.J. Staal, M.D. Abramoff, M. Niemeijer, M.A. Viergever, and B. van Ginneken // IEEE Transactions on Medical Imaging. – 2004. – Vol. 23(4). – P. 501-509.
  20. Gangaputra, S. Retinal vessel caliber among people with acquired immunodeficiency syndrome: relationships with disease-associated factors and mortality / P.S. Kalyani, A.A. Fawzi, M.L. Van Natta, L.D. Hubbard, R.P. Danis, J.E. Thorne, G.N.  Holland // Am. J. Ophthalmol. – 2012. – Vol. 153(3). – P. 434-444.
  21. Gelman, R. Diagnosis of Plus Disease in Retinopathy of Prematurity Using Retinal Image multiScale Analysis / R. Gelman, M.E. Martinez-Perez, D.K. Vanderveen, A. Moskowitz, A.B. Fulton // Investigative Ophthalmology and Visual Science. – 2005. – Vol. 46(12). – P. 4734-4738.
  22. Stanton, A.Vascular network changes in the retina with age and hy­pertension / A.V. Stanton, B. Wasan, A. Cerutti, S. Ford, R. Marsh, P.P. Sever, S.A. Thom, A.D. Hughes // J. Hypertens. – 1995. – Vol. 13(12). – P. 1724-1728.
  23. Chapman, N. Computer algorithms for the automated measurement of retinal arteriolar diameters / N. Witt, X. Gao, A.A. Bharath, A.V. Stanton, S.A. Thom, A.D. Hughes // Br. J. Ophthalmol. – 2001. – Vol. 85(1). – P. 74-79.
  24. Wong, T.Y. Retinal microvascular abnormalities and incident stroke: the atherosclerosis risk in communities study / T.Y. Wong, R. Klein, D.J. Couper, L.S. Cooper, E.  Shahar, L.D.  Hubbard , M.R.  Wofford, A.R.  Sharrett // Lancet. – 2001. – Vol. 358(9288). – P. 1134-1140.
  25. McClintic, B.R. The relationship between retinal microvascular abnormalities and coronary heart disease: a review / B.R. McClintic, J.I. McClintic, J.D. Bisognano, R.C. Block // Am. J. Med. – 2010. – Vol. 123(4). – P. 374.e1-374.e7.
  26. Chaudhuri, S. Detection of Blood Vessels in Retinal Images Using Two-Dimensional Matched Filters / S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, M. Goldbaum // IEEE Transactions on Medical Imaging. – 1989. – Vol. 8(3). – P. 263-269.
  27. Goldbaum, M. Automated diagnosis and image understanding with object extraction, object classification, and inferencing in retinal images / M. Goldbaum, S. Moezzi, A. Taylor, S. Chatterjee, J. Boyd, E. Hunter, R. Jain // IEEE conference on ICIP. – 1996. – Vol. 3. – P. 695-698.
  28. Li, Q. Colour Retinal Image Segmentation For Computer-aided Fundus Diagnosis Department of Computing / Qin Li. – The Hong Kong Polytechnic University, 2010. – 126 p.
  29. Pai, R. Automated Diagnosis of Retinal Images Using Evidential Reasoning / R. Pai, A. Hoover, M. Goldbaum. – International Conference on SENG, 2002.
  30. Abramoff, M. Web-based screening for diabetic retinopathy in a primary care population: The eye check project / M. Abramoff, M. Suttorp // Telemedicine and e-Health. – 2005. – Vol. 11(6). – P. 668-674.
  31. Niemeijer, M. Automatic detection of red lesions in digital color fundus photographs / M. Niemeijer, B. van Ginneken, J. Staal, M.S.A.S. Schulten, M.D. Abramoff // IEEE Transactions on Medical Imaging. – 2005.– Vol. 24(5). – P. 584-592.
  32. Soares, J. Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification / J. Soares, J. Leandro, R. Cesar, H. Jelinek, M. Cree // IEEE Transactions of Medical Imaging. – 2006. – Vol. 25(9). – P. 1214-1222.
  33. Sussman, E.J. Diagnosis of diabetic eye disease / E.J. Sussman, W.G. Tsiaras, K.A. Soper // J. Am. Med. Assoc. – 1982.– Vol. 247. – P. 3231-3234.
  34. Klonoff, D. An economic analysis of interventions for diabetes / D. Klonoff, D. Schwartz // Diabetes Care. – 2000. – Vol. 23(3). – P. 390-404.
  35. Bresnick, G. A screening approach to the surveillance of patients with diabetes for the presence of vision-threatening retinopathy / G. Bresnick, D. Mukamel, J. Dickinson, D. Cole // Opthalmology. – 2000. – Vol. 107(1). – P. 19-24.
  36. Wilkinson, C.P. Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales / C.P. Wilkinson, F.L. Ferris, R.E. Klein, P.P. Lee, C.D. Agardh, M. Davis, D. Dills, R. Pararajasegaram, A. Kampik, J.T. Verdaguer // Ophthalmology. – 2003. – Vol. 110(9). – P. 1677-1682.
  37. Hutchins, G.M. Tortuosity as an index of the age and diameter increase of coronary collateral vessels in patients after acute myocardial infarction / G.M. Hutchins, M.M. Miner, B.H. Bulkley // Am. J. Cardiol. – 1978. – Vol. 41(2). – P. 210-215.
  38. Miller, R.G. Retinal vessel diameter and the incidence of coronary artery disease in type 1 diabetes / R.G. Miller, C.T. Prince, R. Klein, T.J. Orchard // Am. J. Ophthalmol. – 2009. – Vol. 147(4). – P. 653-660.
  39. Ugurbas, S.C. Comparison of UK and US screening criteria for detection of retinopathy of prematurity in a developing nation / S.C. Ugurbas, H. Gulcan, H. Canan, H. Ankarali, B. Torer, Y.A. Akova // J. AAPOS. – 2010. – Vol. 14(6). – P. 506-520.
  40. Wilson, C.M. Digital image analysis in retinopathy of prematurity: A comparison of vessel selection methods / C.M. Wilson, K. Wong, N. Jeffery, K.D. Cocker, A. Ells, A.R. Fielder // J. AAPOS. – 2012. – Vol. 16 – P. 223-228.
  41. Ghodasra, D.H. The rate of change in retinal vessel width and tortuosity in eyes at risk for retinopathy of prematurity / D.H. Ghodasra, A. Thuangtong, K.A. Karp, G.S. Ying, M.D. Mills, C.A. Wilson, A.R. Fielder, G.E. Quinn // J. AAPOS. – 2012. – Vol. 16(5). – P. 431-446.
  42. Tereshchenko, A.V. Digital technology for diagnostics of retinopathy of prematurity / A.V. Tereshchenko, M.S. Tereshchenkova, Y.A. White, I.G. Trifanenkova, Y.A. Yudina // Kuban science medical Herald. – 2011. – № 1. – С. 79-82.
  43. Mintz-Hittner, H.A. Treatment of retinopathy of prematurity with vascular endothelial growth factor inhibitors / H.A. Mintz-Hittner // Early Human Development. – 2012. – Vol. 88(12). – P. 937-1041.
  44. Jomier, J. Aylward, quantification of retinopathy of prematurity via vessel segmentation / J. Jomier, K.D. Wallace, R. Stephen // In: MICCAI 2013 proceedings. – 2003. – Vol. 62879, LNCS 2879. – P. 620-626.
  45. Grunwald, L. The rate of retinal vessel dilation in severe retinopathy of prematurity requiring treatment / L. Grunwald, D.M. Mills, S. Keegan, K.A. Karp, G.E. Quinn, Y. Gui-Shuang, J.E. Grunwald // Am. J. Ophthalmol. – 2009. – Vol. 147 (6). – P. 1086-1091.
  46. Shah, D.N. Semiautomated digital image analysis of posterior pole vessels in retinopathy of prematurity / D.N. Shah, С.M. Wilson, G.S. Ying, K.A. Karp, A.R. Fielder, J. Ng, M.D. Mills, G.E. Quinn // J. AAPOS. – 2009. – Vol. 13(5). – P. 504-516.
  47. Wallace, D.K. Computer-automated quantification of plus disease in retinopathy of prematurity / D.K. Wallace, J. Jomier, S.R. Aylward, M.B. Landers // J. AAPOS. – 2003. – Vol. 7(2). – P. 126-130.
  48. Adjeroh, D.A. Texton-based segmentation of retinal vessels/ D.A. Adjeroh, A. Kandaswamy, J.V. Odom // J. Opt. Soc. Am. A: Opt. Image Sci. Vis. (OSA-A). – 2007. – Vol. 24(5). – P. 1384-1393.
  49. Hoover, A. Locating blood vessels in retinal images by piecewise threshold probing of amatchedfilter response / A. Hoover, V. Kouznetsova, M. Goldbaum // IEEE Trans Med Imaging. – 2000. – Vol. 19(3). – P. 203-210.
  50. Newey, V.R. Online artery diameter measurement in ultrasound images using artificial neural networks / V.R. Newey, D.K. Nassiri // Ultrasound Med. Biol. – 2002. – Vol. 28(2). – P. 209-216.
  51. Jan, J. Retinal image analysis aimed at blood vessel tree segmentation and early detection of neural-layer deterioration / J. Jan, J. Odstrcilik, J. Gazarek, R. Kolar // Computerized Medical Imaging and Graphics. – 2012. – Vol. 36(6). – P. 431-441.
  52. heng, G.G. An automatic diabetic retinal image screening system book chapter in medical data mining and knowledge discovery / G.G. Kheng, H.S. Wynne, M. Li, H. Wang // Edited by Krzysztof Cios. – 2001. – Vol. 29. – P. 181-210.
    Iqbal, M.I. Detection of vascular intersection in retina fundus image using modified cross point number and neural network technique / A.M.  Aibinu, M.  Nilsson, I.B.  Tijani more authors // Int. Conf. Comput. Commun. Eng. – 2008. – P. 241-246.
  53. A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features / D. Marin, A. Aquino, M.E. Gegundez-Arias, J.M. Bravo // IEEE Transactions on Medical Imaging. – 2011. – Vol. 30(1). – P. 146-158.
  54. Nearest-neighbor methods in learning and vision: theory and practice / S. Gregory, D. Trevor, I. Piotr // Neural Information Processing / MIT Press, 2006.
  55. Beck, T. Robust model-based centerline extraction of vessels in CTA data / T. Beck, C. Biermann, D. Fritz, R. Dillmann // Proceedings of SPIE. – 2009. – Vol. 7259. – 72593O(9 pp). – doi:10.1117/12.810753.
  56. Sinthanayothin, C. Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images / C. Sinthanayothin, J. Boyce, H. Cook, T. Williamson // British Journal of Ophthalmology. – 1999. – Vol. 83(8). – P. 902-910.
  57. Niemeijer, M. Comparative study of retinal vessel segmentation methods on a new publicly available database / M. Niemeijer, J.J. Staal, B. van Ginneken, M. Loog, M.D. Abrаmoff // SPIE Medical imaging. – 2004. – Vol. 5370. – P. 648-656.
  58. Ablameyko, S.V. Fast method of extraction of the network of vessels on gray angiography images / S.V. Ablameyko, A.M. Nedzved, A.M. Belotserkovsky, T.M. Lehmann // Artificial Intelligence. – 2006. – Vol. 2 – P. 206-211.
  59. Rangayyan, R. Detection of the optic nerve head in fundus images of the retina with gabor filters and phase portrait analysis / R. Rangayyan, X. Zhu, F. Ayres, A. Ells // Journal of Digital Imaging. – 2010. – Vol. 23(4). – P. 438-453.
  60. Liu, Z.Q. Handwriting Recognition: Soft Computing and Probabilistic Approaches / Z.Q. Liu, J. Cai, R. Buse // Studies in Fuzziness and Soft Computing / Berlin, Heidelberg, New York: Springer, 2003. – Vol. 133. – P. 31-57.
  61. Deemter, J.H. Simultaneous detection of lines and edges using compound gabor filters / J.H. Deemter, J.M.H. Du Buf // International Journal of Pattern Recognition and Artificial Intelligence. – 2000. – Vol. 14(6). – P. 757-777.
  62. Ricci, E. Retinal blood vessel segmentation using line operators and support vector classification / E. Ricci, R. Perfetti // IEEE Transactions on Medical Imaging. – 2007. – Vol. 26(10). – P. 1357-1365.
  63. Mookiaha, M.R.K. Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features / M.R.K. Mookiaha, U.R. Acharyaa, C.M. Lima, A. Petznickb, S. Jasjit // Knowledge-Based Systems. – 2012. – Vol. 33. – P. 73-82.
  64. Xu, L. A novel method for blood vessel detection from retinal images / L. Xu, S. Luo // BioMedical Engineering. – 2010. – Vol. 9(1). – P. 9-14.
  65. Osareh, A. Automatic blood vessel segmentation in color images of retina / A. Osareh, B.  Shadgar // Iranian Journal Of Science And Technology Transaction. – 2009. – Vol. 23(B2). – P. 191-206.
  66. Martinez-Perez, M.E. Retinal blood vessel segmentation by means of scale-space analysis and region growing / M.E. Martinez-Perez, A.D. Hughes, A.V. Stanton, S.A. Thom, A.A. Bharath, K.H. Parker. –Proc. 2nd MICCAI, 1999. – Vol. 1679. – P. 90-97. – ISBN:3-540-66503-X.
  67. Martinez-Perez, M.E. Segmentation of blood vessels from red-free and fluorescein retinal images / M.E. Martinez-Perez, A.D. Hughes, S.A. Thom, A.A.  Bharath, K.H.  Parker // Medical Image Analysis. – 2007. – Vol. 11(1). – P. 47-61.
  68. Tolias, Y.A. A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering / Y.A. Tolias, S.M. Panas // IEEE Transactions on Medical Imaging. – 1998. – Vol. 17(2). – P. 263-273.
  69. Salem, S.A. Segmentation of retinal blood vessels using a novel clustering algorithm (RACAL) with a partial supervision strategy / S.A. Salem, N.A. Salem, A.K. Nandi // Medical and Biological Engineering and Computing. – 2007. – Vol. 45(3). – P. 261-273.
  70. Kande, G.B. Unsupervised fuzzy based vessel segmentation in pathological digital fundus images / G.B. Kande, P.V. Subbaiah, T.S. Savithri // Journal of Medical Systems. – 2010. – Vol. 34(5). – P. 849-858.
  71. Villalobos-Castaldi, F. A fast, efficient and automated method to extract vessels from fundus images / F. Villalobos-Castaldi, E. Felipe-Riverуn, L.  Sánchez-Fernández // Journal of Visualization. – 2010. – Vol. 13(3). – P. 263-270.
  72. Ng, J. Maximum likelihood estimation of vessel parameters from scale space analysis / J. Ng, S.T. Clay, S.A. Barman, A.R. Fielder, M.J. Moseley, K.H. Parker, C. Paterson // Image and Vision Computing. – 2010. – Vol. 28(1). – P. 55-63.
  73. Condurache, A. P. Segmentation of retinal vessels with a hysteresis binary-classification paradigm / A.P. Condurache, A. Mertins // Computerized Medical Imaging and Graphics. – 2012. – Vol. 36(4). – P. 325-335.
  74. Adel, M. Statistical-based tracking technique for linear structures detection: Application to vessel segmentation in medical images / M.  Adel, A. Moussaoui, M. Rasigni, S. Bourennane, L. Hamami // IEEE Signal Process. – 2010. – Vol. 17(6). – P. 555-558. – ISSN 1070-9908.
  75. Xinge, Y. Segmentation of retinal blood vessels using the radial projection and semi-supervised approach / Y. Xinge, Q. Peng, Y. Yuan, Y. Cheung, J. Lei // Pattern Recognition. – 2011. – Vol. 44(10-11). – P. 2314-2324.
  76. Kochner, B. Course tracking and contour extraction of retinal vessels from color fundus photographs: Most efficient use of steerable filters from model based image analysis / B. Kochner, D. Schuh­mann, M. Michaelis, G. Mann, K.H. Englmeier // Proc. SPIE Medical Imaging. – 1998. – Vol. 3338. – P. 755-761.
  77. Sukkaew, L. Automatic extraction of the structure of the retinal blood vessel network of premature infants / L.B. Uyyanonvara, S.A. Barman, A. Fielder, K. Cocker // Journal of the Medical Association of Thailand. – 2007. – Vol. 90(9). – P. 1780-1792.
  78. Yao, C. Automated retinal blood vessels segmentation based on simplified PCNN and fast 2D-Otsu algorithm / C. Yao, H.-j. Chen // Journal of Central South University of Technology. – 2009. – Vol. 16. – P. 640-646.
  79. Cinsdikici, M.G. Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm / M.G. Cinsdikici, D. Aydin // Computer Methods and Programs in Biomedicine. – 2009. – Vol. 96(2). – P. 85-95.
  80. Bankhead, P. Fast retinal vessel detection and measurement using wavelets and edge location refinement / P. Bankhead, C.N. Schol­field, J.G. McGeown, T.M. Curties // PLoS ONE. – 2012. – Vol. 7(3). – P. 1-12.
  81. Gang, L. Detection and Measurement of Retinal Vessels in Fundus Images Using Amplitude Modified Second-Order Gaussian Filter / L. Gang, O. Chutatape, S.M. Krishnan // IEEE Tran. Biomedical Engineering. – 2002. – Vol. 49(2). – P. 168-172.
  82. Al-Rawi, M. An improved matched filter for blood vessel detection of digital retinal images / M. Al-Rawi, M. Qutaishat, M. Arrar // Computers in Biology and Medicine. – 2007. – Vol. 37(2). – P. 262-267.
  83. Zhang, B. Retinal vessel extraction by matched filter with first-order derivative of Gaussian / B. Zhang, L. Zhang, L. Zhang, F. Karray // Computers in Biology and Medicine. – 2010. – Vol. 40(4). – P. 438-445.
  84. Zana, F. Registration Algorithm of Eye Fundus Images Using Vessels Detection and Hough Transform / F. Zana, J.C. Klein // IEEE Transactions on Medical Imaging. – 1999. – Vol. 18(5). – P. 419-428.
  85. Zana, F. Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation / F. Zana, J.C. Klein // IEEE Trans Image Processing. – 2002. – Vol. 10(7). – P. 1010-1019.
  86. Ayala, G. Different averages of a fuzzy set with an application to vessel segmentation / G. Ayala, T. Leon, V. Zapater // IEEE Transactions on Fuzzy Systems. – 2005. – Vol. 13(3). – P. 384-393.
  87. Miri, M.S. Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction / M.S. Miri, A. Mahloojifar // IEEE Transactions on Biomedical Engineering. – 2011. – Vol. 58(5). – P. 1183-1192.
  88. Calvo, D. Automatic detection and characterisation of retinal vessel tree bifurcations and crossovers in eye fundus images / M. Ortega, M.G. Penedo, J. Rouco // Computer Methods and Programs in Bio­medicine. – 2011. – Vol. 103(1). – P. 28-38.
  89. Fraz, M.M. An approach to localize the retinal blood vessels using bit planes and centerline detection / M.M. Fraz, S.A. Barman, P. Remagnino, A. Hoppe, A. Basit, B. Uyyanonvara, A.R. Rudnicka, C.G. Owen // Comput Methods Programs Biomed. – 2012. – Vol. 108(2). – P. 600-616.
  90. Bouraoui, B. 3D segmentation of coronary arteries based on advanced mathematical morphology techniques / B. Bouraoui, C. Ronse, J. Baruthio, N. Passat, P. Germain // Computerized Medical Imaging and Graphics. – 2010. – Vol. 34(5). – P. 377-387.
  91. Yang, Y. An automatic hybrid method for retinal blood vessel extraction / Y. Yang, S. Huang, N. Rao // International Journal of Applied Mathematics and Computer Science. – 2008. – Vol. 18(3). – P. 399-407.
  92. Sun, K. Morphological multiscale enhancement, fuzzy filter and watershed for vascular tree extraction in angiogram / K. Sun, Z. Chen, S. Jiang, Y. Wang // Journal of Medical Systems. – 2011. – Vol. 35(5). – P. 811-824.
  93. Nasonov, A.V. Application of the method of morphological amoebas to blood vessels in the eye detection fundus images / A. Nasonov, A.A. Chernomorets, A.S. Krylov, A.S. Rodin // DSPA'2011. – 2011. – Vol. 2. – P. 158-161.
  94. Mendonca, A.M. Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction / A.M. Mendonca, A. Campilho // IEEE Transactions on Medical Imaging. – 2006. – Vol. 25(9). – P. 1200-1213.
  95. Li, Q. Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses / Q. Li, Y. Jane, D. Zhang / Expert Systems with Applications. – 2012. – Vol. 39(9). – P. 7600-7610.
  96. Nguyen, U.T.V. An effective retinal blood vessel segmentation method using multi-scale line detection / U.T.V. Nguyen, A. Bhuiyan, L.A.F. Park, K. Ramamohanarao // Pattern Recognition. – 2013. – Vol. 46(3). – P. 703-715.
  97. Moghimirad, E. Retinal vessel segmentation using a multi-scale medialness function / E. Moghimirad, S.H. Rezatofighi, H. Soltanian-Zadeh // Computers in Biology and Medicine. – 2012. – Vol. 42(1). – P. 50-60.
  98. Läthén, G. Blood vessel segmentation using multi-scale quadrature filtering / G. Läthén, J. Jonasson, M. Borga // Pattern Recognition Letters. – 2010. – Vol. 31(8). – P. 762-767.
  99. Frangi, A.F. Multiscale vessel enhancement filtering, in: Medical Image Computing and Computer-Assisted Interventation / A.F. Frangi, W.J. Niessen, K.L. Vincken, M.A. Viergever. – MICCAI’98, 1998. – Vol. 1496. – P. 130-137.
  100. Wink, O. Multiscale vessel tracking / O. Wink, W.J. Niessen, M.A. Viergever // IEEE Trans Med Imaging. – 2004. – Vol. 23(1). – P. 130-133.
  101. Sofka, M. Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures / M. Sofka, C.V. Stewart // IEEE Transactions on Medical Imaging. – 2006. – Vol. 25(12). – P. 1531-1546.
  102. Anzalone, A. A modular supervised algorithm for vessel segmentation in red-free retinal images / A. Anzalone, F. Bizzarri, M. Parodi, M. Storace // Computers in Biology and Medicine. – 2008. – Vol. 38(8). – P. 913-922.
  103. Biesdorf, A. Segmentation and quantification of the aortic arch using joint 3D model-based segmentation and elastic image registration / A. Biesdorf, K. Rohr, D. Feng, H. von Tengg-Kobligk, F. Rengier, D. Böckler, H.U. Kauczor, S. Wörz // Medical Image Analysis. – 2012. – Vol. 16(6). – P. 1187-1201.
  104. Vermeer, K.A. A model based method for retinal blood vessel detection / K.A. Vermeer, F.M. Vos, H.G. Lemij, A.M. Vossepoel // Comput Biol Med. – 2004. – Vol. 34(3). – P. 209-219.
  105. Mahadevan, V. Robust model-based vasculature detection in noisy biomedical images / V. Mahadevan, H. Narasimha-Iyer, B. Roy­sam, H.L. Tanenbaum // IEEE Transactions on Information Technology in Biomedicine. – 2004. – Vol. 8(3). – P. 360-376.
  106. Can, A. Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms / A. Can, J.N. Turner, H.L. Tanenbaum, B. Roysam // IEEE Transactions on Information Technology in Biomedicine. – 1999. – Vol. 3(2). – P. 125-138.
  107. NarasimhaIyer, H. Improved detection of the central reflex in retinal vessels using a generalized dual-gaussian model and robust hypothesis testing / H. Narasimha-Iyer, V. Mahadevan, J.M. Beach, B. Roysam // IEEE Transactions on Information Technology in Biomedicine. – 2008. – Vol. 12(3). – P. 406-410.
  108. Wang, L. Analysis of retinal vasculature using a multiresolution Hermite model / L. Wang, A. Bhalerao, R. Wilson // IEEE Transactions on Medical Imaging. – 2007. – Vol. 26(2). – P. 137-152.
  109. Narasimha-Iyer, H. Automatic identification of retinal arteries and veins from dual-wavelength images using structural and functional features / H. Narasimha-Iyer, J.M. Beach, B. Khoobehi, B. Roysam // IEEE Transactions on Biomedical Engineering. – 2007. – Vol. 54. – P. 1427-1435.
  110. Xiaohong, G. A method of vessel tracking for vessel diameter measurement on retinal images / G. Xiaohong, A. Bharath, A. Stanton, A. Hughes, N. Chapman, S. Thom // International Conference on Image Processing. – 2001. – Vol. 2. – P. 881-884.
  111. Lam, B.S.Y. General retinal vessel segmentation using regularization-based multiconcavity modeling / B.S.Y. Lam, G. Yongsheng, A.W.C. Liew // IEEE Transactions on Medical Imaging. – 2010. – Vol. 29(7). – P. 1369-1381.
  112. Zhu, T. Fourier cross-sectional profile for vessel detection on retinal images / V. Mahadevan, H. Narasimha-Iyer, B. Roysam // Computerized Medical Imaging and Graphics. – 2010. – Vol. 34(3). – P. 203-212.
  113. KovesiP. Phase congruency detects corners and edges / P. Kovesi. – The Australian Pattern Recognition Societys Conference: DICTA2003, 2003. – P. 309-318.
  114. Osareh, A. Color morphology and snakes for optic disc localization / A. Osareh, М. Mirmehdi, B. Thomas, R. Markham // Pattern Recognition. – 2002. – Vol. 1. – P. 743-746.
  115. Xu, J. Optic disk feature extraction via modified deformable model technique for glaucoma analysis / J. Xu, O. Chutatape, E. Sung, C. Zheng, Paul Chew Tec Kuan // Pattern Recognition. – 2007. – Vol. 40(7). – P. 2063-2076.
  116. Hsiao, H.K. novel optic disc detection scheme on retinal images / H.K. Hsiao, C.C. Liu, C.Y. Yu, S.W. Kuo, S.S. Yu // Expert Systems with Applications. – 2012. – Vol. 39(12).– P. 10600-10606.
  117. Espona, L. A snake for retinal vessel segmentation / L. Espona, M.J. Carreira, M. Ortega, M.G. Penedo // Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis / International Conference on Pattern Recognition, 2007. – Vol. 4478. – P. 178-185.
  118. Espona, L. Retinal vessel tree segmentation using a deformable contour model / L. Espona, M.J. Carreira, M.G. Penedo, M. Ortega. – ICPR International Conference on Pattern Recognition, 2008. – Vol. 5197 – P. 683-690.
  119. Al-Diri, B. A ribbon of twins for extracting vessel boundaries / B. Al-Diri, A. Hunter. – EMBEC'05 The 3rd European Medical and Biological Engineering Conference, 2005. – Vol. 11(1).
  120. Al-Diri, B. An active contour model for segmenting and measuring retinal vessels / B. Al-Diri, A. Hunter, D. Steel // IEEE Transactions on Medical Imaging. – 2009. – Vol. 28(9).– P. 1488-1497.
  121. Al-Diri, B. REVIEW – a reference data set for retinal vessel profiles, in: Engineering in Medicine and Biology Society / B. Al-Diri, A. Hunter, D. Steel, M. Habib, T. Hudaib, S. Berry. – EMBS Annual International Conference of the IEEE, 2008. – Vol. 2008. – P. 2262-2265.
  122. Jiang, X. Structural performance evaluation of curvilinear structure detection algorithms with application to retinal vessel segmentation / M. Lambers, H. Bunke // Pattern Recognition Letters. – 2012. – Vol. 33(15). – P. 2048-2056.
  123. Zhang, Y. Detection of retinal blood vessels based on nonlinear projections / Y. Zhang, W. Hsu, M. Lee // Journal of Signal Processing Systems. – 2009. – Vol. 55(1-3). – P. 103-112.
  124. Tong, C.S. Variational image binarization and its multi-scale realizations / C.S. Tong, Y. Zhang, N. Zheng // Journal of Mathematical Imaging and Vision. – 2005. – Vol. 23(2). – P. 185-198.
  125. Lam, B.Y. A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields / B.Y. Lam, Y. Hong // IEEE Transactions on Medical Imaging. – 2008. – Vol. 27(2). – P. 237-246.
  126. Ramlugun, G.S. Small retinal vessels extraction towards proliferative diabetic retinopathy screening / G.S. Ramlugun, V.K. Nagarajan, C. Chakraborty // Expert Systems with Applications. – 2012. – Vol. 39(1). – P. 1141-1146.
  127. Rapantzikos, K. Detection and segmentation of drusen deposits on human retina: potential in the diagnosis of age-related macular degeneration / K. Rapantzikos, M. Zervakis, K. Balas // Med Imaging Anal. – 2003. – Vol. 7(1). – P. 95-108.
  128. Azemin, M.Z. Robust methodology for fractal analysis of the retinal vasculature / M.Z. Azemin, D.K. Kumar, T.Y. Wong, R. Kawasaki, P. Mitchell, J.J. Wang // IEEE Transactions on Medical Imaging. – 2011. – Vol. 30(2). – P. 243-250.
  129. Patasius, M. Recursive Algorithm for Blood Vessel Detection in Eye Fundus Images / M. Patasius, V. Marozas, D. Jegelevieius, A. Lukosevieius // IFMBEProceedings – 2009. – Vol. 25(11). – P. 212-215.
  130. Ilyasova, N.Yu. Information technologies of image analysis in medical diagnostics / N.Yu. Ilyasova, A.V. Kupriyanov, A.G. Khramov. – Moscow: "Radio i svyaz" Publisher, 2012. – 424 p.
  131. Korepanov, A.O. A method of allocating the Central lines of blood vessels of the diagnostic images / A.O. Korepanov, N.Yu. Ilyasova, M. Chikulaev // Computer Optics. – 2006. – Vol. 29. – P. 146-151.
  132. Aylward, S.R. Intensity ridge and widths for tabular object segmentation and registration / S.R. Aylward, S. Pizer, E. Bullitt and D. Eberl // Wksp on Math Image Analysis. – 1996. – Vol. 7. – P. 131-138. – ISBN: 0-8186-7367-2.
  133. Lecornu, L. Extraction of vessel contours in angiograms by simul- taneous tracking of the two edges / L. Lecornu, C. Roux, J.J. Jacq // Engineering in Medicine and Biology Society. – 1994. – Vol. 1. – P. 678-679.
  134. Hart, M. A method of automated coronary artey tracking in unsubtracted angiograms / M. Hart, L. Holley // IEEE Computers in Cardiology. – 1993. – P. 93-96.
  135. Chutatape, O. Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters / O. Chutatape, L. Zheng, S.M. Krishman // Proceedings of IEEE EMBS. – 1998. – Vol. 6.– P. 3144-3149.
  136. Poon, K. Live-vessel: extending livewire for simultaneous extraction of optimal medial and boundary paths in vascular images / K.Poon, G.Hamarneh, R.Abugharbieh // Medical Image Computing and Computer-Assisted Intervention MICCAI-2007, 2007. – Vol. 4792 – P. 444-451.
  137. Barrett, W.A. Interactive live-wire boundary extraction / W.A. Bar­rett, E.N. Mortensen // Medical Image Analysis. – 1997. – Vol. 1(4) – P. 331-341.
  138. Delibasis, K.K. Automatic model-based tracing algorithm for vessel segmentation and diameter estimation / K.K. Delibasis, A.I. Kechriniotis, C. Tsonos, N. Assimakis // Comput. Methods Programs Biomed. – 2010. – Vol. 100(2). – P. 108-122.
  139. Vlachos, M. Multi-scale retinal vessel segmentation using line tracking / M. Vlashos, E. Dermatas // Computerized Medical Imaging and Graphics. – 2010. – Vol. 34(3). – P. 213-227.
  140. Lalonde, Т. Non-recursive paired tracking for vessel extraction from retinal images / M. Lalonde, L. Gagnon, M.C. Bouchert. – Montreal: Proceedings of the Conference Vision Interface 2000, 2000. – P. 61-68.
  141. Yin, Y. Retinal vessel segmentation using a probabilistic tracking method / Y. Yin, M. Adel, S. Bourennane // Pattern Recognition. – 2012. – Vol. 45(4). – P. 1235-1244.
  142. Huang, Y. An automated computational framework for retinal vascular network labeling and branching order analysis / Y. Huang, J. Zhang, Y. Huang // Microvascular Research. – 2012. – Vol. 84(2). – P. 169-177.
  143. Gao, X. Measurement of vessel diameters on retinal images for cardiovascular studies / X. Gao, A. Bharath, A. Stanton, A. Hughes, N. Chapman, S. Thom. – On-line Conference Proceedings: Medical Image Understanding and Analysis, 2001.
  144. Liu, Y.P. Retinal arterio lar and venular phenoty pes in a Flemish populati on: Repro ducibility and correlates / Y.P. Liu, T. Richart, Y. Jin, H.A. Struijker-Boundierc, J.A. Staessen // Artery Research. – 2011. – Vol. 5(2). – P. 72-79.
  145. Wong, T.Y. Retinal vascular caliber, cardiovascular risk factors, and inflammation: the multi-ethnic study of atherosclerosis (MESA) / T.Y. Wong, F.M. Islam, R. Klein, B.E. Klein, M.F. Cotch, C. Castro, A.R. Sharrett, E. Shahar // Invest. Ophthalmol. Vis. Sci. – 2006. – Vol. 47(6). – P. 2341-2350.
  146. Ikram, M.K. Retinal vessel diameters and risk of impaired fasting glucose or diabetes / M.K. Ikram, J.A. Janssen, A.M. Roos, I. Riet­veld, J.C. Witteman, M.M. Breteler, A. Hofman, C.M. van Duijn, P.T. de Jong // Diabetes. – 2006. – Vol. 55(2). – P. 506-510.
  147. Newsom, R.S. Retinal vessel measurement: comparison between observer and computer driven methods / R.S. Newsom, P.M. Sul­livan, S.M. Rassam, R. Jagoe, E.M. Kohner // Graefes Arch. Clin. Exp. Ophthalmol. – 1992. – Vol. 230(3). – P. 221-225.
  148. Hiroki, M. Tortuosity of the white matter medullary arterioles is related to the severity of hypertension / M. Hiroki, K. Miyashita, M. Oda // Cerebrovasc Dis. – 2002. – Vol. 13(4). – P. 242-250.
  149. King, L.A. Arteriolar length-diameter (L:D) ratio: a geometric parameter of the retinal vasculature diagnostic of hypertension / L.A. King, A.V. Stanton, P.S. Sever, S.A. Thom, A.D. Hughes // J. Hum. Hypertens. – 1996. – Vol. 10(6). – P. 417-418.
  150. Rassam, S.M. Accurate vessel width measurement from fundus photographs: a new concept / S.M. Rassam, V. Patel, O. Brinch­mann-Hansen, O. Engvold, E.M. Kohner // British Journal of Ophthalmology. – 1994. – Vol. 78(1). – P. 24-29.
  151. Brinchmann-Hansen, O. Microphotometry of the blood column and light streak on retinal vessels in fundus photographs / O. Brinchmann-Hansen, O. Engvold // Acta Ophthalmologica. – 1986. – Vol. 64(S179). – P. 9-19.
  152. Gao, X.W. Quantification characterisation of arteries in retinalimages / X.W. Gao, A. Bharath, A. Stanton, A. Hughes, N. Chapman, S. Thom // Computer Methods and Programs in Biomedicine. – 2000. – Vol. 63(2). – P. 133-146.
  153. Pedersen, L. Quantitative measurement of changes in retinal vessel diameter in ocular fundus images / L. Pedersen, M. Grunkin, B. Ersboll, K. Madsen, M. Larsen, N. Christoffersen, U. Skands // Pattern Recognition Letters. – 2000. – Vol. 21(13-14). – P. 1215-1223.
  154. Wong, T.Y. A prospective cohort study of retinal arteriolar narrowing and mortality / T.Y. Wong, M.D. Knudtson, R. Klein [et al.] // Am. J. Epidemiol. – 2004. – Vol. 159(9). – P. 819-825.
  155. Yiming, W. A fast method for automated detection of blood vessels in retinal images / W. Yiming, Y. Wang, C. Lee // Signals, Systems & Computers. – 1997. – Vol. 2. – P. 1700-1704.
  156. Fathi, A. Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation / A. Fathi, A.R. Naghsh-Nilchi // Biomedical Signal Processing and Control. – 2013. – Vol. 8(1). – P. 71-80.
  157. Vilser, W. Automated measurement of retinal vascular diameter / W. Vilser, S. Klein, P. Wulff, G. Fuchs // Fortschr. Ophthalmol. – 1991. – Vol. 88(5). – P. 482-486.
  158. Tyrrell, J.A. 2-D/3-D model-based method to quantify the complexity of microvasculature imaged by in vivo multiphoton microscopy / V. Mahadeva, T. Tong, E.B. Brown, R.K. Jain, B. Roysam // Microvascular Research. – 2005. – Vol. 70(3) – P. 165-178.
  159. Hanssen, H. Retinal vessel diameter, obesity and metabolic risk factors in school children (JuvenTUM 3) / H.  Hanssen , M.  Siegrist , M.  Neidig , A.  Renner , P.  Birzele , A.  Siclovan , K.  Blume , C.  Lammel , B.  Haller , A. Schmidt-Trucksäss , M. Halle // Atherosclerosis. – 2012. – Vol. 221(1).– P. 242-248.
  160. Saez, M. Development of an automated system to classify retinal vessels into arteries and veins / M.  Saez, S.  González-Vázquez, M.  González-Penedo, M.A.  Barceló, M.  Pena-Seijo , G.  Coll de Tuero , A.  Pose-Reino // Comput. Methods Programs Biomed. – 2012. – Vol. 108(1). – P. 367-376.
  161. Mosher, A. Comparison of Retinal Vessel Measurements in Digital vs Film Imags / A. Mosher, E.K. Klein, R. Klein, M.D. Knudtson, N.J. Ferrier // Am. J.  Ophthalmol. – 2006. – Vol. 142(5). – P. 875-878.
  162. Muramatsu, C. Automated selection of major arteries and veins for measurement of arteriolar-to-venular diameter ratio on retinal fundus images / C.  Muramatsu, Y.  Hatanaka, T.  Iwase, T.  Hara, Н.  Fujita // Computerized Medical Imaging an Graphics. – 2011. – Vol. 35(6).– P. 472-480.
  163. Knudtson, M.D. Revised formulas for summarizing retinal vessel diameters / M.D. Knudtson, K.E. Lee, L.D. Hubbard, T.Y. Wong, R. Klein, B.E. Klein // Curr. Eye. Res. – 2003. – Vol. 27(3) – P. 143-149.
  164. Tramontan, L. Computer estimation of the AVR parameter in diabetic retinopathy / L. Tramontan, A. Ruggeri // IFMBE Proc. – 2009. – Vol. 25(11) – P. 141-154.
  165. Nam, H.S. Automated measurement of retinal vessel diameters on digital fundus photographs / H.S. Nam, J.M. Hwang, H. Chung, J.M. Seo // IFMBE Proc. – 2009. – Vol. 25(11) – P. 277-280.
  166. Niemeijer, M. Automatic determination of the artery–vein ratio in retinal fundus images / M. Niemeijer, B. van Ginneken, M.D. Abra­moff // Proc SPIE. – 2010. – Vol. 7624
  167. Niall, P. Retinal image analysis: Concepts, applications and potential / P. Niall, M.A. Tariq, M. Thomas, J.D. Ian, D. Baljean, I.E. Ro­bert, Y. Kanagasingam, J.C. Ian // Progress in Retinal and Eye Research. – 2006. – Vol. 25(1). – P. 99-127.
  168. Sharrett, L.D. Retinal arteriolar diameters and elevated blood pressure: the atherosclerosis risk in communities study / L.D. Hubbard, L.S. Cooper, P.D. Sorlie, R.J. Brothers, F.J. Nieto, J.L. Pinsky, R. Klein // American Journal Epidemiology. – 1999. – Vol. 150(3) – P. 263-270.
  169. Muramatsu, C. Automated segmentation of optic disc region on retinal fundus photographs: comparison of contour modeling and pixel classification methods / C. Muramatsu, T. Nakagawa, A. Sawada, Y. Hatanaka, T. Hara, T.Yamamoto // Comput Methods Programs Biomed. – 2011. – Vol. 101(1). – P. 23-32. MESSIDOR: Methods for Evaluating Segmentation and Indexing techniques Dedicated to Retinal Ophthalmology [Электронный ресурс], 2004. – http://messidor.crihan.fr/index-en.php.
  170. Kauppi, V. DIARETDB1 diabetic retinopathy database and evaluation protocol / T. Kauppi, V. Kalesnykiene, J.K. Kamarainen, L. Lensu, I. Sorri, A. Raninen, R. Voutilainen, J. Pietilд, H. Kдlviдinen, H. Uusitalo. – Medical Image Understanding and Aberystwyth (MIUA2007), 2007. – Vol. 41 - P. 61-65.
  171. ARIA Online, Retinal Image Archive [Электронный ресурс]: http://www.eyecharity.com/aria online/. – 2006.
  172. The VICAVR database [Электронный ресурс]: http://www.var­pa.es/vicavr.html. – 2010.
  173. Chapman, N. Retinal vascular tree morphology: a semi-automatic quantification / N. Chapman, M.E. Martinez-Perez, A.D. Highes, A.V. Stanton, S.A. Thorn, A.A. Bharath, K.H. Parker // IEEE Trans. Biomedical Engineering. – 2002. – Vol. 49(8). – P. 912-917.
  174. Dougherty, G. A quantitative index for the measurement of the tortuosity of blood vessels / G. Dougherty, J. Varro // Medical Engineering & Physics. – 2000. – Vol. 22(8). – P. 567-574.
  175. Johnson, M.J. Robust measures of three-dimensional vascular tortuosity based on the minimum curvature of approximating polynomial spline fits to the vessel mid-line / M.J. Johnson , G. Dougherty // Med. Eng. Phys. – 2007. – Vol. 29(6).– P. 677-690.
  176. Spangler, K.M. Arteriolar tortuosity of the white matter in aging and hypertension / V.R. Challa, D.M. Moody, M.A. Bell // A microradiographic study. J. Neuropathol. Exp. Neurol. – 1994. – Vol. 53(1). – P. 22-26.
  177. Hart, W.E. Measurement and classification of retinal vascular tortuosity / W.E. Hart, M. Goldbaum, B.  Côté, P. Kube, M.R. Nelson // International Journal of Medical Informatics. – 1999. – Vol. 53(2-3). – P. 239-252.
  178. Capowski, J.J. A numeric index based on spatial frequency for the tortuosity of retinal vessels and its application to plus disease in retinopathy of prematurity / J.J. Capowski, J.A. Kylstra, S.F. Freed­man // Retina. – 1995. – Vol. 15(6) – P. 490-500.
  179. Swanson, C. Semiautomated computer analysis of vessel growth in preterm infants without and with ROP / C. Swanson, K.D. Cocker, K.H. Parker, M.J. Moseley, A.R. Fielder // Br. J. Ophthalmol. – 2003. – Vol. 87(12). – P. 1474-1477.
  180. Ilyasova, N.Yu. Biomechanical characteristics of blood vessels for digital image analysis fundus / N.Yu. Ilyasova, A.V. Kupriyanov, N.A. Gavrilova, G.A. Shilkin, N.I. Lanevskaya // Biomehanika glaza. – 2002. – P. 18-30.
  181. Lotmar, W. Measurement of vessel tortuosity on fundus photographs / W. Lotmar, A. Freiburghaus, D. Bracher // Graefe’s Arch. Clin. Exp Ophthalmol. – 1979. – Vol. 221(1). – P. 49-57.
  182. Bracher, D. Changes in peripapillary tortuosity of the central retinal arteries in newborns / D. Bracher // Graefe’s Arch. Clin. Exp Ophthalmol. – 1982. – Vol. 218(4). – P. 211-217.
  183. Brinkman, A.M. Variability of human coronary artery geometry: an angiographic study of the left anterior descending arteries of 30 autopsy hearts / A.M.  Brinkman, P.B.  Baker, W.P.  Newman, R.  Vigorito, M.H.  Friedman // Ann Biomed Eng. – 1994. – Vol. 22(1). – P. 34-44.
  184. Smedby, O. Tortuosity and atherosclerosis in the femoral artery: what is cause and what is effect / O. Smedby, L. Bergstrand // Ann. Biomed. Eng. – 1996. – Vol. 24(4). – P. 474-480.
  185. Smedby, O. Two-dimensional tortuosity of the superficial femoral artery in early atherosclerosis / O. Smedby, N.  Högman, S.  Nilsson, U.  Erikson, A.G.  Olsson, G.  Walldius // J. Vasc. Res. – 1993. – Vol. 30(4). – P. 181-191.
  186. Kimball, B. Angiographic features associated with acute coronary artery occlusion during elective angioplasty / B. Kimball, , S. Bui, N. Dafopoulos. J. Can // Cardiol. – 1990. – Vol. 6(8) – P. 327-332.
  187. Cheung, C.Y. Retinal Vascular Tortuosity, Blood Pressure, and Cardiovascular Risk / C.Y.  Cheung, Y. Zheng, W. Hsu, M.L. Lee, Q.P. Lau, P. Mitchell, J.J. Wang, R. Klein, T.Y. Wong // Ophthalmology. – 2011. – Vol. 118(5). – P. 812-818.
  188. Martin Rodriguez, Z. Improved characterisation of aortic tortuosity / Z. Martin Rodriguez, P. Kenny, L. Gaynor // Med. Eng. Phys. – 2011. – Vol. 33(6). – P. 712-719.
  189. Wenn, C.M. Arterial tortuosity / C.M. Wenn, D.L Newman // Phys. Eng. Sci. Med. – 1990. – Vol. 13(2) – P. 67-70.
  190. Kaupp, A. Automatic evaluation of retinal vessel width and tortuosity in digital ?uorescein angiograms, Invest / A. Kaupp, H. Toonen, S. Wolf, K. Schulte, R. Effert, D. Meyer-Ebrecht, M. Reim // Ophthalmol. – 1991. – Vol. 84. –P. 952-987.
  191. Bullitt, E. Analyzing attributes of vessel populations / E. Bullitt, K.E. Muller, I. Jung, W. Lin, S. Aylward // Med. Image Anal. – 2005. – Vol. 9(1). – P. 39-49.
  192. Grisan, E A novel method for the automatic evaluation of retinal vessel tortuosity / E.  Grisan , M.  Foracchia , A.  Ruggeri // IEEE Trans. Med. Imaging Proc. – 2008. – P. 310-319.
  193. Chandrinos, K.V. Image processing techniques for the quantification of atherosclerotic changes / K.V. Chandrinos, R.B. Fisher, P.E. Traha­nias // Proc. MEDICO98 / Cyprus: Limassol, 1998.
  194. Kylstra, J.A. The relationship between retinal vessel tortuosity, diameter and transmural pressure / T. Wierzbicki, M.L. Wolbarsht, M.B. Landers III, E. Stefansson // Clin. Exp. Ophthalmol. – 1986. – Vol. 224(5).– P. 477-480.
  195. Sasongko, M.B. Alterations in retinal microvascular geometry in young type 1 diabetes / J.J. Wang, K.C. Donaghue, N. Cheung, A.J. Jenkins, P. Benitez-Aguirre, J.J. Wang // Diabetes Care. – 2010. – Vol. 33(6). – P. 1331-1336.
  196. Leung, H. Does hormone replacement therapy influence retinal microvascular caliber? / H. Leung, J.J. Wang, E. Rochtchina, T.Y. Wong, R. Klein, P. Mitchell // Microvascular Research. – 2004. – Vol. 67(1). – P. 48-54.
  197. Gregson, P.H. Automated grading of venous beading / Z. Shen, R.C. Scott, V. Kozousek // Computers And Biomedical. – 1995. – Vol. 28(4). – P. 291-304.
  198. Hunter, A. Non-linear Filtering for vascular segmentation and detection of venous beading / A. Hunter, J. Lowell, D. Steel, A. Basu and R. Ryder // Tech. report, University of Durham. – 2003. – P.100-104.
  199. Abrаmoff, M.D. Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes / M.D. Abrаmoff, M. Niemeijer, M.S. Suttorp-Schulten, M.A. Viergever, S.R. Russell, B. van Ginneken // Diabetes Care. – 2008. – Vol. 31(2). – P. 193-198.

© 2009, IPSI RAS
Institution of Russian Academy of Sciences, Image Processing Systems Institute of RAS, Russia, 443001, Samara, Molodogvardeyskaya Street 151; e-mail: ko@smr.ru; Phones: +7 (846 2) 332-56-22, Fax: +7 (846 2) 332-56-20