Retinal Vessels Segmentation Techniques and Algorithms: A Survey
Source abstract
Abstract
Retinal vessels identification and localization aim to separate the different retinal vasculature structure tissues, either wide or narrow ones, from the fundus image background and other retinal anatomical structures such as optic disc, macula, and abnormal lesions. Retinal vessels identification studies are attracting more and more attention in recent years due to non-invasive fundus imaging and the crucial information contained in vasculature structure which is helpful for the detection and diagnosis of a variety of retinal pathologies included but not limited to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD). With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting retinal vessels are becoming more and more crucial and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for retinal vessels segmentation techniques. Firstly, a brief introduction to retinal fundus photography and imaging modalities of retinal images is given. Then, the preprocessing operations and the state of the art methods of retinal vessels identification are introduced. Moreover, the evaluation and validation of the results of retinal vessels segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for retinal vessels identification techniques.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Retinal Imaging and Analysis · Digital Imaging for Blood Diseases · Glaucoma and retinal disorders
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Computer Science and Engineering Department, University of Bridgeport, 126 Park Ave, Bridgeport, CT 06604, USA
Computer Science Department, William Paterson University, 300 Pompton Rd, Wayne, NJ 07470, USA
References (100+)
Show source-provided references
1. A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes10.1016/j.media.2009.07.011 ↗
2. A review of vessel extraction techniques and algorithms10.1145/1031120.1031121 ↗
3. Kirbas, C., and Quek, F.K. (2003, January 12). Vessel extraction techniques and algorithms: A survey. Proceedings of the the Third IEEE symposium on Bioinformatics and Bioengineering, Bethesda, MD, USA.
4. A review on MR vascular image processing: Skeleton versus nonskeleton approaches: Part II10.1109/TITB.2002.804136 ↗
5. Blood vessel segmentation methodologies in retinal images—A survey10.1016/j.cmpb.2012.03.009 ↗
6. Recent Advancements in Retinal Vessel Segmentation10.1007/s10916-017-0719-2 ↗
7. Dash, J., and Bhoi, N. (2015, January 12–13). A Survey on Blood Vessel Detection Methodologies in Retinal Images. Proceedings of the 2015 International Conference on Computational Intelligence and Networks, Bhubaneshwar, India.10.1109/CINE.2015.39 ↗
8. Evolutionary Computing Enriched Computer Aided Diagnosis System For Diabetic Retinopathy: A Survey10.1109/RBME.2017.2705064 ↗
9. Pohankar, N.P., and Wankhade, N.R. (March, January 29). Different methods used for extraction of blood vessels from retinal images. Proceedings of the 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave), Coimbatore, India.10.1109/STARTUP.2016.7583909 ↗
10. Singh, N., and Kaur, L. (2015, January 29–30). A survey on blood vessel segmentation methods in retinal images. Proceedings of the 2015 International Conference on Electronic Design, Computer Networks & Automated Verification (EDCAV), Shillong, India.10.1109/EDCAV.2015.7060532 ↗
11. Kolb, H. (2018, January 22). Simple Anatomy of the Retina,. Available online: http://webvision.med.utah.edu/book/part-i-foundations/simple-anatomy-of-the-retina/.
12. Oloumi, F., Rangayyan, R.M., Eshghzadeh-Zanjani, P., and Ayres, F. (2007, January 22–26). Detection of blood vessels in fundus images of the retina using gabor wavelets. Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France. Available online: http://people.ucalgary.ca/~ranga/enel697/.10.1109/IEMBS.2007.4353836 ↗
13. Saine, P.J., and Tyler, M.E. (2002). Ophthalmic Photography: Retinal Photography, Angiography, and Electronic Imaging, Butterworth-Heinemann.
14. Kolb, H., Fernandez, E., and Nelson, R. (1995). Simple Anatomy of the Retina. Webvision: The Organization of the Retina and Visual System, University of Utah Health Sciences Center. Available online: http://europepmc.org/books/NBK11533;jsessionid=4C8BAD63F75EAD49C21BC65E2AE5F6F3.
15. (2018, January 22). Ophthalmic Photographers’ Society. Available online: www.opsweb.org.
16. Ng, E., Acharya, U.R., Rangayyan, R.M., and Suri, J.S. (2014). Ophthalmological Imaging and Applications, CRC Press.10.1201/b17026 ↗
17. Color Fundus Photography Versus Fluorescein Angiography in Identification of the Macular Center and Zone in Retinopathy of Prematurity10.1016/j.ajo.2015.01.027 ↗
18. A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images10.1109/TBME.2016.2535311 ↗
19. Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores10.1109/TMI.2016.2587062 ↗
20. Retrieving challenging vessel connections in retinal images by line co-occurrence statistics10.1007/s00422-017-0718-x ↗
21. Curvature integration in a 5D kernel for extracting vessel connections in retinal images10.1109/TIP.2017.2761543 ↗
22. Gu, L., and Cheng, L. (2015, January 7–13). Learning to boost filamentary structure segmentation. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.10.1109/ICCV.2015.80 ↗
23. A graph-theoretical approach for tracing filamentary structures in neuronal and retinal images10.1109/TMI.2015.2465962 ↗
24. Maninis, K.-K., Pont-Tuset, J., Arbeláez, P., and Van Gool, L. (2016). Deep retinal image understanding. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer International Publishing.10.1007/978-3-319-46723-8_17 ↗
25. Sironi, A., Lepetit, V., and Fua, P. (2015, January 7–13). Projection onto the manifold of elongated structures for accurate extraction. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.10.1109/ICCV.2015.44 ↗
26. Solouma, N., Youssef, A.-B.M., Badr, Y., and Kadah, Y.M. Real-time retinal tracking for laser treatment planning and administration. Medical Imaging 2001: Image Processing, SPIE—The International Society of Optics and Photonics.10.1117/12.431010 ↗
27. Wang, Y., and Lee, S.C. (1997, January 2–5). A fast method for automated detection of blood vessels in retinal images. Proceedings of the Conference Record of the Thirty-First Asilomar Conference on Signals, Systems & Computers, Pacific Grove, CA, USA.
28. Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms10.1109/4233.767088 ↗
29. Li, H., and Chutatape, O. (2000, January 23–28). Fundus image features extraction. Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA.
30. An automatic hybrid method for retinal blood vessel extraction10.2478/v10006-008-0036-5 ↗
31. Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images10.1109/TMI.2015.2409024 ↗
32. Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets10.1109/TITB.2012.2198668 ↗
33. Vascular intersection detection in retina fundus images using a new hybrid approach10.1016/j.compbiomed.2009.11.004 ↗
34. Wu, C.-H., Agam, G., and Stanchev, P. (2007, January 12–15). A hybrid filtering approach to retinal vessel segmentation. Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA, USA.10.1109/ISBI.2007.356924 ↗
35. Siddalingaswamy, P., and Prabhu, K.G. (2010). Automatic detection of multiple oriented blood vessels in retinal images. J. Biomed. Sci. Eng., 3.10.4236/jbise.2010.31015 ↗
36. Hierarchical retinal blood vessel segmentation based on feature and ensemble learning10.1016/j.neucom.2014.07.059 ↗
37. Kauppi, T., Kalesnykiene, V., Kamarainen, J.-K., Lensu, L., Sorri, I., Raninen, A., Voutilainen, R., Uusitalo, H., Kälviäinen, H., and Pietilä, J. (2007, January 10–13). The DIARETDB1 Diabetic Retinopathy Database and Evaluation Protocol. Proceedings of the British Machine Vision Conference 2007, Coventry, UK.10.5244/C.21.15 ↗
38. A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina10.1109/TMI.2002.806290 ↗
39. The meaning and use of the area under a receiver operating characteristic (ROC) curve10.1148/radiology.143.1.7063747 ↗
40. Receiver operating characteristic analysis: A tool for the quantitative evaluation of observer performance and imaging systems10.1016/j.jacr.2006.02.021 ↗
41. Ridge-based vessel segmentation in color images of the retina10.1109/TMI.2004.825627 ↗
42. Niemeijer, M., Staal, J., van Ginneken, B., Loog, M., and Abramoff, M.D. (2004, January 15–17). Comparative study of retinal vessel segmentation methods on a new publicly available database. Proceedings of the Medical Imaging 2004: Physics of Medical Imaging, San Diego, CA, USA.10.1117/12.535349 ↗
43. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response10.1109/42.845178 ↗
44. Bankhead, P., Scholfield, C.N., McGeown, J.G., and Curtis, T.M. (2012). Fast retinal vessel detection and measurement using wavelets and edge location refinement. PLoS ONE, 7.10.1371/journal.pone.0032435 ↗
45. (2018, January 22). MESSIDOR: Methods for Evaluating Segmentation and Indexing Techniques Dedicated to Retinal Ophthalmology, 2004. Available online: http://www.adcis.net/en/Download-Third-Party/Messidor.html.
46. Feedback on a publicly distributed image database: The Messidor database10.5566/ias.1155 ↗
47. Retinal vessel segmentation by improved matched filtering: Evaluation on a new high-resolution fundus image database10.1049/iet-ipr.2012.0455 ↗
48. Detection of blood vessels in retinal images using two-dimensional matched filters10.1109/42.34715 ↗
49. Chanwimaluang, T., and Fan, G. (2003, January 14–17). An efficient algorithm for extraction of anatomical structures in retinal images. Proceedings of the 2003 International Conference on Image Processing (Cat. No.03CH37429), Barcelona, Spain.
50. An improved matched filter for blood vessel detection of digital retinal images10.1016/j.compbiomed.2006.03.003 ↗
51. A fast, efficient and automated method to extract vessels from fundus images10.1007/s12650-010-0037-y ↗
52. Retinal vessel extraction by matched filter with first-order derivative of Gaussian10.1016/j.compbiomed.2010.02.008 ↗
53. Zhu, T., and Schaefer, G. (September, January 30). Retinal vessel extraction using a piecewise Gaussian scaled model. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA.
54. Automated detection of retinal blood vessels in diabetic retinopathy using Gabor filter
55. Cauchy Based Matched Filter for Retinal Vessels Detection10.4103/2228-7477.128432 ↗
56. Singh, N.P., Kumar, R., and Srivastava, R. (2015, January 15–16). Local entropy thresholding based fast retinal vessels segmentation by modifying matched filter. Proceedings of the International Conference on Computing, Communication & Automation, Noida, India.10.1109/CCAA.2015.7148552 ↗
57. Kumar, D., Pramanik, A., Kar, S.S., and Maity, S.P. (2016, January 12–15). Retinal blood vessel segmentation using matched filter and laplacian of gaussian. Proceedings of the 2016 International Conference on Signal Processing and Communications (SPCOM), Bangalore, India.10.1109/SPCOM.2016.7746666 ↗
58. Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter10.1016/j.cmpb.2016.03.001 ↗
59. Chutatape, O., Liu, Z., and Krishnan, S.M. (1998, January 1). Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters. Proceedings of the the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol.20 Biomedical Engineering towards the Year 2000 and Beyond (Cat. No.98CH36286), Hong Kong, China.
60. Retinal Vessel Centerline Extraction Using Multiscale Matched Filters, Confidence and Edge Measures10.1109/TMI.2006.884190 ↗
61. Adel, M., Rasigni, M., Gaidon, T., Fossati, C., and Bourennane, S. (2009, January 7–10). Statistical-based linear vessel structure detection in medical images. Proceedings of the 2009 16th IEEE International Conference on Image Processing (ICIP), Cairo, Egypt.10.1109/ICIP.2009.5413396 ↗
62. Wu, C.H., Agam, G., and Stanchev, P. (2007, January 20–23). A general framework for vessel segmentation in retinal images. Proceedings of the 2007 International Symposium on Computational Intelligence in Robotics and Automation, Jacksonville, FL, USA.10.1109/CIRA.2007.382924 ↗
63. Yedidya, T., and Hartley, R. (2008, January 1–3). Tracking of Blood Vessels in Retinal Images Using Kalman Filter. Proceedings of the 2008 Digital Image Computing: Techniques and Applications, Canberra, Australia.10.1109/DICTA.2008.72 ↗
64. Yin, Y., Adel, M., Guillaume, M., and Bourennane, S. (2010, January 26–29). A probabilistic based method for tracking vessels in retinal images. Proceedings of the 2010 IEEE International Conference on Image Processing, Hong Kong, China.10.1109/ICIP.2010.5650937 ↗
65. Li, H., Zhang, J., Nie, Q., and Cheng, L. (2013, January 19–21). A retinal vessel tracking method based on bayesian theory. Proceedings of the 2013 8th IEEE Conference on Industrial Electronics and Applications (ICIEA), Melbourne, Australia.
66. Budai, A., Michelson, G., and Hornegger, J. (2010, January 14–16). Multiscale Blood Vessel Segmentation in Retinal Fundus Images. Proceedings of the Bildverarbeitung für die Medizin, Aachen, Germany.
67. Moghimirad, E., Rezatofighi, S.H., and Soltanian-Zadeh, H. (2010, January 14–17). Multi-scale approach for retinal vessel segmentation using medialness function. Proceedings of the 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Rotterdam, The Netherlands.10.1109/ISBI.2010.5490423 ↗
68. Abdallah, M.B., Malek, J., Krissian, K., and Tourki, R. (2011, January 22–25). An automated vessel segmentation of retinal images using multiscale vesselness. Proceedings of the Eighth International Multi-Conference on Systems, Signals & Devices, Sousse, Tunisia.
69. Rattathanapad, S., Mittrapiyanuruk, P., Kaewtrakulpong, P., Uyyanonvara, B., and Sinthanayothin, C. (2012, January 5–7). Vessel extraction in retinal images using multilevel line detection. Proceedings of the 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, Hong Kong, China.10.1109/BHI.2012.6211584 ↗
70. Kundu, A., and Chatterjee, R.K. (December, January 30). Retinal vessel segmentation using Morphological Angular Scale-Space. Proceedings of the 2012 Third International Conference on Emerging Applications of Information Technology, Kolkata, India.10.1109/EAIT.2012.6407935 ↗
71. Frucci, M., Riccio, D., Baja, G.S.D., and Serino, L. (2014, January 23–27). Using Contrast and Directional Information for Retinal Vessels Segmentation. Proceedings of the 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems, Marrakech, Morocco.10.1109/SITIS.2014.18 ↗
72. Fast, accurate and robust retinal vessel segmentation system10.1016/j.bbe.2017.04.001 ↗
73. Dizdaro, B., Ataer-Cansizoglu, E., Kalpathy-Cramer, J., Keck, K., Chiang, M.F., and Erdogmus, D. (2012, January 23–26). Level sets for retinal vasculature segmentation using seeds from ridges and edges from phase maps. Proceedings of the 2012 IEEE International Workshop on Machine Learning for Signal Processing, Santander, Spain.10.1109/MLSP.2012.6349730 ↗
74. Jin, Z., Zhaohui, T., Weihua, G., and Jinping, L. (2015, January 27–29). Retinal vessel image segmentation based on correlational open active contours model. Proceedings of the 2015 Chinese Automation Congress (CAC), Wuhan, China.
75. Gongt, H., Li, Y., Liu, G., Wu, W., and Chen, G. (2015, January 14–16). A level set method for retina image vessel segmentation based on the local cluster value via bias correction. Proceedings of the 2015 8th International Congress on Image and Signal Processing (CISP), Shenyang, China.10.1109/CISP.2015.7407915 ↗
76. Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images10.1109/TPAMI.2003.1159954 ↗
77. Akram, M.U., Tariq, A., and Khan, S.A. (2009, January 15–16). Retinal image blood vessel segmentation. Proceedings of the 2009 International Conference on Information and Communication Technologies, Doha, Qatar.10.1109/ICICT.2009.5267194 ↗
78. A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images10.1016/j.compmedimag.2016.06.001 ↗
79. Back-propagation network and its configuration for blood vessel detection in angiograms10.1109/72.363449 ↗
80. Salem, S.A., Salem, N.M., and Nandi, A.K. (2006, January 4–8). Segmentation of retinal blood vessels using a novel clustering algorithm. Proceedings of the 2006 14th European Signal Processing Conference, Florence, Italy.
81. Xie, S., and Nie, H. (2013, January 16–18). Retinal vascular image segmentation using genetic algorithm Plus FCM clustering. Proceedings of the 2013 Third International Conference on Intelligent System Design and Engineering Applications (ISDEA), Hong Kong, China.10.1109/ISDEA.2012.289 ↗
82. A Novel Retinal Blood Vessel Segmentation Algorithm using Fuzzy segmentation
83. Emary, E., Zawbaa, H.M., Hassanien, A.E., Schaefer, G., and Azar, A.T. (2014, January 6–11). Retinal vessel segmentation based on possibilistic fuzzy c-means clustering optimised with cuckoo search. Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China.10.1109/IJCNN.2014.6889932 ↗
84. Maji, D., Santara, A., Ghosh, S., Sheet, D., and Mitra, P. (2015, January 25–29). Deep neural network and random forest hybrid architecture for learning to detect retinal vessels in fundus images. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.10.1109/EMBC.2015.7319030 ↗
85. Sharma, S., and Wasson, E.V. (2015). Retinal Blood Vessel Segmentation Using Fuzzy Logic. J. Netw. Commun. Emerg. Technol., 4.
86. Roy, A.G., and Sheet, D. (arXiv, 2016). DASA: Domain Adaptation in Stacked Autoencoders using Systematic Dropout, arXiv.10.1109/ACPR.2015.7486600 ↗
87. Lahiri, A., Roy, A.G., Sheet, D., and Biswas, P.K. (2016, January 16–20). Deep neural ensemble for retinal vessel segmentation in fundus images towards achieving label-free angiography. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.10.1109/EMBC.2016.7590955 ↗
88. Maji, D., Santara, A., Mitra, P., and Sheet, D. (arXiv, 2016). Ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images, arXiv.
89. Segmenting Retinal Blood Vessels With Deep Neural Networks10.1109/TMI.2016.2546227 ↗
90. An ensemble classification-based approach applied to retinal blood vessel segmentation10.1109/TBME.2012.2205687 ↗
91. Dasgupta, A., and Singh, S. (arXiv, 2016). A Fully Convolutional Neural Network based Structured Prediction Approach Towards the Retinal Vessel Segmentation, arXiv.10.1109/ISBI.2017.7950512 ↗
92. Automatic grading of retinal vessel caliber10.1109/TBME.2005.847402 ↗
93. Yu, H., Barriga, S., Agurto, C., Nemeth, S., Bauman, W., and Soliz, P. (2013). Automated retinal vessel type classification in color fundus images. Proc. SPIE, 8670.10.1117/12.2006444 ↗
94. Ma, Z., and Li, H. (2015, January 21–24). Retinal vessel profiling based on four piecewise Gaussian model. Proceedings of the 2015 IEEE International Conference on Digital Signal Processing (DSP), Singapore.10.1109/ICDSP.2015.7252048 ↗
95. Fourier cross-sectional profile for vessel detection on retinal images10.1016/j.compmedimag.2009.09.004 ↗
96. Lenskiy, A.A., and Lee, J.S. (2010, January 27–30). Rugged terrain segmentation based on salient features. Proceedings of the ICCAS 2010, Gyeonggi-do, Korea.10.1109/ICCAS.2010.5669787 ↗
97. Salem, N.M., and Nandi, A.K. (2008, January 16–19). Unsupervised Segmentation of Retinal Blood Vessels Using a Single Parameter Vesselness Measure. Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, Bhubaneswar, India.10.1109/ICVGIP.2008.34 ↗
98. The watershed transform: Definitions, algorithms and parallelization strategies10.3233/FI-2000-411207 ↗
99. Serra, J. (1982). Image Analysis and Mathematical Morphology, v. 1, Academic Press.
100. Scale-space theory: A basic tool for analyzing structures at different scales10.1080/757582976 ↗