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Recent Advancements in Retinal Vessel Segmentation

  • Image & Signal Processing
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Abstract

Retinal vessel segmentation is a key step towards the accurate visualization, diagnosis, early treatment and surgery planning of ocular diseases. For the last two decades, a tremendous amount of research has been dedicated in developing automated methods for segmentation of blood vessels from retinal fundus images. Despite the fact, segmentation of retinal vessels still remains a challenging task due to the presence of abnormalities, varying size and shape of the vessels, non-uniform illumination and anatomical variability between subjects. In this paper, we carry out a systematic review of the most recent advancements in retinal vessel segmentation methods published in last five years. The objectives of this study are as follows: first, we discuss the most crucial preprocessing steps that are involved in accurate segmentation of vessels. Second, we review most recent state-of-the-art retinal vessel segmentation techniques which are classified into different categories based on their main principle. Third, we quantitatively analyse these methods in terms of its sensitivity, specificity, accuracy, area under the curve and discuss newly introduced performance metrics in current literature. Fourth, we discuss the advantages and limitations of the existing segmentation techniques. Finally, we provide an insight into active problems and possible future directions towards building successful computer-aided diagnostic system.

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References

  1. Yau, J. W. Y., et al., Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 35.3:556–564, 2012.

    Article  Google Scholar 

  2. Wong, W. L., et al., xGlobal prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. The Lancet Global Health 2.2:e106–e116, 2014.

    Article  Google Scholar 

  3. Tham, Y.-C., et al., Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology 121.11:2081–2090, 2014.

    Article  Google Scholar 

  4. Abramoff, M. D., Garvin, M. K., Sonka, M., Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3:169–208, 2010.

    Article  PubMed  Google Scholar 

  5. Fraz, M. M., et al., Blood vessel segmentation methodologies in retinal images–a survey. Comput. Methods Prog. Biomed. 108.1:407–433, 2012.

    Article  Google Scholar 

  6. Zhao, Y., et al., Retinal vessel segmentation: An efficient graph cut approach with retinex and local phase. PloS One 10.4:e0122332, 2015.

    Article  Google Scholar 

  7. Wong, T. Y., et al., Retinal microvascular changes and MRI signs of cerebral atrophy in healthy, middle-aged people. Neurology 61.6:806–811, 2003.

    Article  Google Scholar 

  8. Doubal, F. N., Hokke, P. E., Wardlaw, J. M., Retinal microvascular abnormalities and stroke: a systematic review. J. Neurol. Neurosurg. Psychiatry 80.2:158–165, 2009.

    Article  Google Scholar 

  9. Smith, W., et al., Retinal arteriolar narrowing is associated with 5-year incident severe hypertension the Blue Mountains Eye Study. Hypertension 44.4:442–447, 2004.

    Article  Google Scholar 

  10. Wong, T. Y., et al., Retinal microvascular abnormalities and their relationship with hypertension, cardiovascular disease, and mortality. Surv. Ophthalmol. 46.1:59–80, 2001.

    Article  Google Scholar 

  11. Wong, T., and Mitchell, P., The eye in hypertension. Lancet 369.9559:425–435, 2007.

    Article  Google Scholar 

  12. Wong, T. Y., et al., Retinal microvascular abnormalities and incident stroke: the Atherosclerosis Risk in Communities Study. Lancet 358.9288:1134–1140, 2001.

    Article  Google Scholar 

  13. Sharrett, A., et al., Richey Retinal arteriolar diameters and elevated blood pressure the atherosclerosis risk in communities study. Am. J. Epidemiol. 150.3:263–270, 1999.

    Article  Google Scholar 

  14. Tso, M. O. M., and Jampol, L. M., Pathophysiology of hypertensive retinopathy. Ophthalmology 89.10: 1132–1145, 1982.

    Article  Google Scholar 

  15. Hoover, A., and Goldbaum, M., Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans. Med. Imaging 22.8:951–958, 2003.

    Article  Google Scholar 

  16. Welikala, R. A., et al., Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification. Comput. Methods Prog. Biomed. 114.3:247–261, 2014.

    Article  Google Scholar 

  17. Cheung, C. S. Y., et al., Computer-assisted image analysis of temporal retinal vessel width and tortuosity in retinopathy of prematurity for the assessment of disease severity and treatment outcome. J. Am. Assoc. Pediatr. Ophthalmol. Strabismus 15.4:374–380, 2011.

    Article  Google Scholar 

  18. Sutter, F. K. P., and Helbig, H., Familial retinal arteriolar tortuosity: a review. Surv. Ophthalmol. 48.3: 245–255, 2003.

    Article  Google Scholar 

  19. Grisan, E., Foracchia, M., Ruggeri, A., A novel method for the automatic grading of retinal vessel tortuosity. IEEE Trans. Med. Imaging 27.3:310–319, 2008.

    Article  Google Scholar 

  20. Sodi, A., et al., Computer assisted evaluation of retinal vessels tortuosity in Fabry disease. Acta Ophthalmol. 91.2:e113–e119, 2013.

    Article  Google Scholar 

  21. Marín, D., et al., A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Med. Imaging 30.1:146–158, 2011.

    Article  Google Scholar 

  22. Fraz, M. M., et al., An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans. Biomed. Eng. 59.9:2538–2548, 2012.

    Article  Google Scholar 

  23. Azzopardi, G., et al., Trainable COSFIRE filters for vessel delineation with application to retinal images. Med. Image Anal. 19.1:46–57, 2015.

    Article  Google Scholar 

  24. Nguyen, U. T. V., et al., An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recogn. 46.3:703–715, 2013.

    Article  Google Scholar 

  25. Zhao, Y. Q., et al., Retinal vessels segmentation based on level set and region growing. Pattern Recogn. 47.7:2437–2446, 2014.

    Article  Google Scholar 

  26. Mendonca, A. M., and Campilho, A., Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans. Med. Imaging 25.9:1200–1213, 2006.

    Article  Google Scholar 

  27. Narasimha-Iyer, H., et al., Improved detection of the central reflex in retinal vessels using a generalized dual-Gaussian model and robust hypothesis testing. IEEE Trans. Inf. Technol. Biomed. 12.3:406–410, 2008.

    Article  Google Scholar 

  28. Fraz, M. M., et al., Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification. Int. J. Comput. Assist. Radiol. Surg. 9.5:795– 811, 2014.

    Article  Google Scholar 

  29. Ricci, E., and Perfetti, R., Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imaging 26.10:1357–1365, 2007.

    Article  Google Scholar 

  30. Azzopardi, G., and Petkov, N., Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters. Pattern Recogn. Lett. 34.8:922–933, 2013.

    Article  Google Scholar 

  31. Sigursson, E. M., et al., Automatic retinal vessel extraction based on directional mathematical morphology and fuzzy classification. Pattern Recogn. Lett. 47:164–171, 2014.

    Article  Google Scholar 

  32. Fraz, M. M., et al., Quantification of blood vessel calibre in retinal images of multi-ethnic school children using a model based approach. Comput. Med. Imaging Graph. 37.1:48–60, 2013.

    Article  Google Scholar 

  33. Chaudhuri, S., et al., Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imaging 8.3:263–269, 1989.

    Article  Google Scholar 

  34. Gang, L., Chutatape, O., Krishnan, S. M., Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter. IEEE Trans. Biomed. Eng. 49.2:168–172, 2002.

    Article  Google Scholar 

  35. Frangi, A. F., et al.: Multiscale vessel enhancement filtering. International Conference on Medical Image Computing and Computer-Assisted Intervention Springer Berlin Heidelberg (1998)

  36. Palomera-Pérez, M. A., et al., Parallel multiscale feature extraction and region growing: application in retinal blood vessel detection. IEEE Trans. Inf. Technol. Biomed. 14.2:500–506, 2010.

    Article  Google Scholar 

  37. Bankhead, P., et al., Fast retinal vessel detection and measurement using wavelets and edge location refinement. PloS One 7.3:e32435, 2012.

    Article  Google Scholar 

  38. Fathi, A., and Naghsh-Nilchi, A. R., Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation. Biomed. Signal Process. Control 8.1:71–80, 2013.

    Article  Google Scholar 

  39. Soares, J. V. B., et al., Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans. Med. Imaging 25.9:1214–1222, 2006.

    Article  Google Scholar 

  40. Zhao, Y., et al., Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images. IEEE Trans. Med. Imaging 34.9:1797–1807, 2015.

    Article  Google Scholar 

  41. Vega, R., et al., Retinal vessel extraction using lattice neural networks with dendritic processing. Comput. Biol. Med. 58:20–30, 2015.

    Article  PubMed  Google Scholar 

  42. Roychowdhury, S., Koozekanani, D. D., Parhi, K. K., Iterative vessel segmentation of fundus images. IEEE Trans. Biomed. Eng. 62.7:1738–1749, 2015.

    Article  Google Scholar 

  43. Fraz, M. M., et al., An approach to localize the retinal blood vessels using bit planes and centerline detection. Comput. Methods Prog. Biomed. 108.2:600–616, 2012.

    Article  Google Scholar 

  44. Wang, Y., et al., Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition. Pattern Recogn. 46.8:2117–2133, 2013.

    Article  Google Scholar 

  45. Youssef, D., and Solouma, N. H., Accurate detection of blood vessels improves the detection of exudates in color fundus images. Comput. Methods Prog. Biomed. 108.3:1052–1061, 2012.

    Article  Google Scholar 

  46. Condurache, A. P., and Mertins, A., Segmentation of retinal vessels with a hysteresis binary-classification paradigm. Comput. Med. Imaging Graph. 36.4:325–335, 2012.

    Article  Google Scholar 

  47. Rahebi, J., and Frat, H., Retinal blood vessel segmentation with neural network by using gray-level co-occurrence matrix-based features. J. Med. Syst. 38.8:1–12, 2014.

    Google Scholar 

  48. Fathi, A., and Naghsh-Nilchi, A. R., General rotation-invariant local binary patterns operator with application to blood vessel detection in retinal images. Pattern. Anal. Applic. 17.1:69–81, 2014.

    Article  Google Scholar 

  49. Ganjee, R., Azmi, R., Gholizadeh, B., An improved retinal vessel segmentation method based on high level features for pathological images. J. Med. Syst. 38.9:1–9, 2014.

    Google Scholar 

  50. Orlando, J. I., Prokofyeva, E., Blaschko, M. B., A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE Trans. Biomed. Eng. 64.1:16–27, 2017.

    Article  Google Scholar 

  51. Cheung, C. Y. -L., et al., Microvascular network alterations in the retina of patients with Alzheimer’s disease. Alzheimers Dement. 10.2:135–142, 2014.

    Article  Google Scholar 

  52. Dai, P., et al., A new approach to segment both main and peripheral retinal vessels based on gray-voting and gaussian mixture model. PloS One 10.6:e0127748, 2015.

    Article  Google Scholar 

  53. Roychowdhury, S., Koozekanani, D. D., Parhi, K. K., Blood vessel segmentation of fundus images by major vessel extraction and subimage classification. IEEE J. Biomed. Health Inform. 19.3:1118–1128, 2015.

    Google Scholar 

  54. Wang, S., et al., Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing 149:708–717, 2015.

    Article  Google Scholar 

  55. Li, Q., et al., A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images. IEEE Trans. Med. Imaging 35.1:109–118, 2016.

    Article  Google Scholar 

  56. Liskowski, P., and Krawiec, K., Segmenting Retinal Blood Vessels with Deep Neural Networks. IEEE Trans. Med. Imaging 35.11:2369–2380, 2016.

    Article  Google Scholar 

  57. Maninis, K. -K., et al.: Deep retinal image understanding. International Conference on Medical Image Computing and Computer-Assisted Intervention Springer International Publishing (2016)

  58. Wu, A., et al.: Deep vessel tracking: A generalized probabilistic approach via deep learning. Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on IEEE (2016)

  59. Fu, H., Xu, Y., Wong, D. W. K., Liu, J.: Retinal vessel segmentation via deep learning network and fully-connected conditional random fields, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, 698–701 (2016)

  60. Strisciuglio, N., et al.: Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters. Machine Vision and Applications, 1–13 (2016)

  61. Krause, M., et al., Fast retinal vessel analysis. J. Real-Time Image Proc. 11.2:413–422, 2016.

    Article  Google Scholar 

  62. Kovács, G., and András, H., A self-calibrating approach for the segmentation of retinal vessels by template matching and contour reconstruction. Med. Image Anal. 29:24–46, 2016.

    Article  PubMed  Google Scholar 

  63. Kar, S. S., and Maity, S. P., Blood vessel extraction and optic disc removal using curvelet transform and kernel fuzzy c-means. Comput. Biol. Med. 70:174–189, 2016.

    Article  PubMed  Google Scholar 

  64. Zhang, J., et al.: Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores IEEE Transactions on Medical Imaging (2016)

  65. Yu, H., et al.: Fast vessel segmentation in retinal images using multiscale enhancement and second-order local entropy. SPIE Medical Imaging International Society for Optics and Photonics (2012)

  66. Moghimirad, E., Rezatofighi, S. H., Soltanian-Zadeh, H., Retinal vessel segmentation using a multi-scale medialness function. Comput. Biol. Med. 42.1:50–60, 2012.

    Article  Google Scholar 

  67. Zheng, J., et al.: Retinal image graph-cut segmentation algorithm using multiscale hessian-enhancement-based nonlocal mean filter. Computational and Mathematical Methods in Medicine 2013 (2013)

  68. Budai, A., et al.: Robust vessel segmentation in fundus images. International Journal of Biomedical Imaging 2013 (2013)

  69. Annunziata, R., et al., Leveraging multiscale hessian-based enhancement with a novel exudate inpainting technique for retinal vessel segmentation. IEEE J. Biomed. Health Inform. 20.4:1129–1138, 2016.

    Article  Google Scholar 

  70. Abdallah, M. B., et al.: Automatic extraction of blood vessels in the retinal vascular tree using multiscale medialness. Journal of Biomedical Imaging 2015 (2015)

  71. Zhang, L., Fisher, M., Wang, W., Retinal vessel segmentation using multi-scale textons derived from keypoints. Comput. Med. Imaging Graph. 45:47–56, 2015.

    Article  CAS  PubMed  Google Scholar 

  72. Yin, B., et al., Vessel extraction from non-fluorescein fundus images using orientation-aware detector. Med. Image Anal. 26.1:232–242, 2015.

    Article  Google Scholar 

  73. Christodoulidis, A., et al.: A Multi-scale Tensor Voting Approach for Small Retinal Vessel Segmentation in High Resolution Fundus Images Computerized Medical Imaging and Graphics (2016)

  74. Yin, Y., Adel, M., Bourennane, S., Retinal vessel segmentation using a probabilistic tracking method. Pattern Recogn. 45.4:1235–1244, 2012.

    Article  Google Scholar 

  75. Yin, Y., Adel, M., Bourennane, S.: Automatic segmentation and measurement of vasculature in retinal fundus images using probabilistic formulation. Computational and Mathematical Methods in Medicine 2013 (2013)

  76. Zhang, J., et al., A retinal vessel boundary tracking method based on Bayesian theory and multi-scale line detection. Comput. Med. Imaging Graph. 38.6:517–525, 2014.

    Article  Google Scholar 

  77. De, J., Li, H., Li, C., Tracing retinal vessel trees by transductive inference. BMC Bioinf. 15:1, 2014.

    Article  Google Scholar 

  78. Bekkers, E., et al., A multi-orientation analysis approach to retinal vessel tracking. J. Math. Imaging Vision 49.3:583–610, 2014.

    Article  Google Scholar 

  79. Fraz, M. M., Basit, A., Barman, S. A., Application of morphological bit planes in retinal blood vessel extraction. J. Digit. Imaging 26.2:274–286, 2013.

    Article  Google Scholar 

  80. Imani, E., Javidi, M., Pourreza, H. -R., Improvement of retinal blood vessel detection using morphological component analysis. Comput. Methods Prog. Biomed. 118.3:263–279, 2015.

    Article  Google Scholar 

  81. Mapayi, T., Viriri, S., Tapamo, J. -R.: Adaptive Thresholding Technique for Retinal Vessel Segmentation Based on GLCM-Energy Information. Computational and Mathematical Methods in Medicine 2015 (2015)

  82. Mapayi, T., Viriri, S., Tapamo, J. -R.: Comparative Study of Retinal Vessel Segmentation Based on Global Thresholding Techniques. Computational and Mathematical Methods in Medicine 2015 (2015)

  83. Xiao, Z., Adel, M., Bourennane, S.: Bayesian method with spatial constraint for retinal vessel segmentation. Computational and Mathematical Methods in Medicine 2013 (2013)

  84. Salazar-Gonzalez, A., et al., Segmentation of blood vessels and optic disc in retinal images. IEEE J. Biomed. Health Inform. 2194:1–1, 2014.

    Google Scholar 

  85. Lazar, I., and Hajdu, A., Segmentation of retinal vessels by means of directional response vector similarity and region growing. Comput. Biol. Med. 66:209–221, 2015.

    Article  PubMed  Google Scholar 

  86. Hassanien, A. E., Emary, E., Zawbaa, H. M., Retinal blood vessel localization approach based on bee colony swarm optimization, fuzzy c-means and pattern search. J. Vis. Commun. Image Represent. 31:186–196, 2015.

    Article  Google Scholar 

  87. Frucci, M., et al.: Severe: Segmenting vessels in retina images. Pattern Recognition Letters (2015)

  88. DRIVE: digital retinal images for vessel extraction, http://www.isi.uu.nl/Research/Databases/DRIVE (2004)

  89. Hoover, A. D., Kouznetsova, V., Goldbaum, M., Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19.3:203–210, 2000.

    Article  Google Scholar 

  90. ARIA Online, Retinal Image Archive, http://www.eyecharity.com/ariaonline/ (2006)

  91. Odstrcilik, J., et al., Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database. IET Image Process. 7.4:373–383, 2013.

    Article  Google Scholar 

  92. Klviinen, R., Voutilainen2, J., Pietil4, H., Uusitalo, H., DIARETDB1 diabetic retinopathy database and evaluation protocol. Med. Image Understanding Anal. 2007:61, 2007.

    Google Scholar 

  93. MESSIDOR: Methods for Evaluating Segmentation and Indexing Techniques Dedicated to Retinal Ophthalmology, http://messidor.crihan.fr/index-en.php (2004)

  94. Al-Diri, B., et al.: REVIEW-a reference data set for retinal vessel profiles. 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE (2008)

  95. Pitzer, S. M., Adaptative histogram equalization and its variations. Comp. Vision Graph. Image Pros. 39: 355–368, 1987.

    Article  Google Scholar 

  96. Perez-Rovira, A., et al.: Improving vessel segmentation in ultra-wide field-of-view retinal fluorescein angiograms. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE (2011)

  97. IOSTAR dataset. [Online]. Available: http://www.retinacheck.org (2015)

  98. RC-SLO dataset. [Online]. Available: http://www.retinacheck.org (2015)

  99. Gegúndez-Arias, M. E., et al., A function for quality evaluation of retinal vessel segmentations. IEEE Trans. Med. Imaging 31.2:231–239, 2012.

    Article  Google Scholar 

  100. Alipour, S. H. M., Hossein, R., Mohammadreza, A., A new combined method based on curvelet transform and morphological operators for automatic detection of foveal avascular zone. SIViP 8.2:205–222, 2014.

    Article  Google Scholar 

  101. Abbasi-Sureshjani, S., et al.: Biologically-inspired supervised vasculature segmentation in SLO retinal fundus images. International Conference Image Analysis and Recognition Springer International Publishing (2015)

  102. Nguyen, U. T. V., et al., An automated method for retinal arteriovenous nicking quantification from color fundus images. IEEE Trans. Biomed. Eng. 60.11:3194–3203, 2013.

    Article  Google Scholar 

  103. Oloumi, F., et al., Computer-aided diagnosis of plus disease via measurement of vessel thickness in retinal fundus images of preterm infants. Comput. Biol. Med. 66:316–329, 2015.

    Article  PubMed  Google Scholar 

  104. Staal, J., et al., Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23.4:501–509, 2004.

    Article  Google Scholar 

  105. Foracchia, M., Grisan, E., Ruggeri, A., Luminosity and contrast normalization in retinal images. Med. Image Anal. 9.3:179–190, 2005.

    Article  Google Scholar 

  106. Felberer, F., et al., Imaging of retinal vasculature using adaptive optics SLO/OCT. Biomed. Opt. Express 6.4:1407–1418, 2015.

    Article  Google Scholar 

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Acknowledgments

The authors gratefully acknowledge Prof. Jayanthi Sivaswamy (CVIT, IIIT-Hyderabad), Samrudhdhi Rangrej, Sukanya Kudi, Raghav Mehta (CVIT, IIIT-Hyderabad) and Praveen GB (BITS-Goa) for the fruitful discussions and valuable suggestions in improving the paper.

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  1. Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India

    Chetan L Srinidhi & P Aparna

  2. Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India

    Jeny Rajan

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  1. Chetan L Srinidhi
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L Srinidhi, C., Aparna, P. & Rajan, J. Recent Advancements in Retinal Vessel Segmentation. J Med Syst 41, 70 (2017). https://doi.org/10.1007/s10916-017-0719-2

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