Abstract
The visual exploration of retinal blood vessels assists ophthalmologists in the diagnoses of different abnormalities of the eyes such as diabetic retinopathy, glaucoma, cardiovascular ailment, high blood pressure, arteriosclerosis, and age-related macular degeneration. The manual inspection of retinal vasculature is an extremely challenging and tedious task for medical experts due to the complex structure of an eye, tiny blood vessels, and variation in vessels width. Several automatic retinal vessels extraction techniques have been proposed in contemporary literature, which assist ophthalmologists in the timely identification of an eye disorders. However, due to the fast evolution of such techniques, a comprehensive survey is needed. This survey presents a comprehensive review of such techniques, strategies, and algorithms presented to date. The techniques are classified into logical groups based on the underlying methodology employed for retinal vessel extraction. The performance of existing techniques is reported on the publicly accessible datasets in term of various performance measures, providing a valuable comparison among the techniques. Thus, this survey presents a valuable resource for researchers working toward automatic extraction of retinal vessels.
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References
Cheng E, Du L, Wu Y, Zhu Y, Megalooikonomou V, Ling H (2014) Discriminative vessel segmentation in retinal images by fusing context-aware hybrid features. Mach Vis Appl 25(7):1779–1792
Abràmoff M, Garvin M, Sonka M (2010) Retinal imaging and image analysis. IEEE Rev Biomed Eng 3:169–208
Jelinek H, Cree M (2009) Automated image detection of retinal pathology. CRC Press, Boca Raton
Patton N, Aslam T, MacGillivray T, Deary I, Dhillon B, Eikelboom R, Yogesan K, Constable I (2006) Retinal image analysis: concepts, applications and potential. Prog Retin Eye Res 25(1):99–127
Franklin S, Rajan S (2014) Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images. Biocybern Biomed Eng 34(2):117–124
Fraz M, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka A, Owen C, Barman S (2012) Blood vessel segmentation methodologies in retinal images—a survey. Comput Methods Programs Biomed 108(1):407–433
Jusoh F, Haron H, Ibrahim R, Azemin M (2016) An overview of retinal blood vessels segmentation. Advanced computer and communication engineering technology. Springer, Berlin, pp 63–71
GeethaRamani R, Balasubramanian L (2016) Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis. Biocybern Biomed Eng 36(1):102–118
Garhöfer G, Vilser W (2012) Measurement of retinal vessel diameters. Ocular blood flow. Springer, Berlin, pp 101–122
Niemeijer M, Staal J, Ginneken BV, Loog M, Abramoff M (2004) Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Medical imaging. International society for optics and photonics, pp 648–656
Dai P, Luo H, Sheng H, Zhao Y, Li L, Wu J, Zhao Y, Suzuki K (2015) A new approach to segment both main and peripheral retinal vessels based on gray-voting and Gaussian mixture model. PLoS ONE 10(6):e0127748
Mabrouk M, Solouma N, Kadah Y (2006) Survey of retinal image segmentation and registration. GVIP J 6(2):1–11
Winder R, Morrow P, McRitchie I, Bailie J, Hart P (2009) Algorithms for digital image processing in diabetic retinopathy. Comput Med Imaging Graph 33(8):608–622
Faust O, Acharya R, Ng E, Ng K, Suri J (2012) Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review. J Med Syst 36(1):145–157
W H Organization (2016) Global report on diabetes. In: WHO Library Cataloguing-in-Publication Data. http://apps.who.int/iris/bitstream/10665/204871/1/9789241565257_eng.pdf
Alberti K, Zimmet P (1998) Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus, Provisional report of a WHO consultation. Diabet Med 15(7):539–553
Reaven G (1998) Role of insulin resistance in human disease. Diabetes 37(12):1595–1607
Ong G, Ripley L, Newsom R, Cooper M, Casswell A (2004) Screening for sight-threatening diabetic retinopathy: comparison of fundus photography with automated color contrast threshold test. Am J Ophthalmol 137(3):445–452
Tielsch J, Katz J, Singh K, Quigley H, Gottsch J, Javitt J, Sommer A (1991) A population-based evaluation of glaucoma screening: the Baltimore eye survey. Am J Epidemiol 134(10):1102–1110
Heijl A, Leske M, Bengtsson B, Hyman L, Bengtsson B, Hussein M (2002) Reduction of intraocular pressure and glaucoma progression: results from the early manifest glaucoma trial. Arch Ophthalmol 120(10):1268–1279
Brothers RHL, King W, Clegg L, Klein R, Cooper L, Sharrett A, Davis M, Cai J (1999) Atherosclerosis risk in communities study group. methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the atherosclerosis risk in communities study. Ophthalmology 106(12):2269–2280
Lim L, Mitchell P, Seddon J, Holz F, Wong T (2012) Age-related macular degeneration. The Lancet 379(9827):1728–1738
Wong C, Yanagi Y, Lee W, Ogura Y, Yeo I, Wong T, Cheung C (2016) Age-related macular degeneration and polypoidal choroidal vasculopathy in Asians. Prog Retinal Eye Res 53:107–139
Frangi A, Niessen W, Vincken K, Viergever M (1998) Multiscale vessel enhancement filtering. In: Medical image computing and computer-assisted interventation—MICCAI’98. Springer, Berlin Heidelberg, pp 130–137
Sofka M, Stewart C (2006) Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures. IEEE Trans Med Imaging 25(12):1531–1546
Hoover A, Kouznetsova V, Goldbaum M (2000) Locating blood vessels in retinal images by piece-wise threhsold probing of a matched filter response. IEEE Trans Med Imaging 19(3):203–210
Martínez-Pérez M, Hughes A, Stanton A, Thom S, Bharath A, Parker K (1999) Retinal blood vessel segmentation by means of scale-space analysis and region growing. In: medical image computing and computer-assisted intervention—MICCAI’99. Springer, Berlin Heidelberg, pp 90–97
Martinez-Perez M, Hughes A, Thom S, Bharath A, Parker K (2007) Segmentation of blood vessels from red-free and fluorescein retinal images. Med Image Anal 11(1):47–61
Farnell D, Hatfield F, Knox P, Reakes M, Spencer S, Parry D, Harding S (2008) Enhancement of blood vessels in digital fundus photographs via the application of multiscale line operators. J Frankl Inst 345(7):748–765
Vlachos M, Dermatas E (2010) Multi-scale retinal vessel segmentation using line tracking. Comput Med Imaging Graph 34(3):213–227
Li Q, You J, Zhang D (2012) Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses. Expert Syst Appl 39(9):7600–7610
Moghimirad E, Rezatofighi S, Soltanian-Zadeh H (2012) Retinal vessel segmentation using a multi-scale medialness function. Comput Biol Med 42(1):50–60
Yu H, Barriga S, Agurto C, Zamora G, Bauman W, Soliz P (2012) Fast vessel segmentation in retinal images using multiscale enhancement and second-order local entropy. In: SPIE medical imaging. International society for optics and photonics, pp 83151B–83151B
Ricci E, Perfetti R (2007) Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans Med Imaging 26(10):1357–1365
Nguyen U, Bhuiyan A, Park L, Ramamohanarao K (2013) An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recognit 46(3):703–715
Fathi A, Naghsh-Nilchi A (2013) Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation. Biomed Signal Process Control 8(1):71–80
Azzopardi G, Petkov N (2013) Trainable COSFIRE filters for keypoint detection and pattern recognition. IEEE Trans Pattern Anal Mach Intell 35(2):490–503
Akram M, Khan S (2013) Multilayered thresholding-based blood vessel segmentation for screening of diabetic retinopathy. Eng Comput 29(2):165–173
Wang Y, Ji G, Lin P, Trucco E (2013) Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition. Pattern Recognit 46(8):2117–2133
Ganjee R, Azmi R, Gholizadeh B (2014) An improved retinal vessel segmentation method based on high level features for pathological images. J Med Syst 38(9):1–9
Zhang B, Zhang L, Zhang L, Karray F (2010) Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput Biol Med 40(4):438–445
Mapayi T, Viriri S, Tapamo J (2014) A new adaptive thresholding technique for retinal vessel segmentation based on local homogeneity information. In: Image and signal processing. Springer, pp 558–567
Ravichandran C, Raja J (2014) A fast enhancement/thresholding based blood vessel segmentation for retinal image using contrast limited adaptive histogram equalization. J Med Imaging Health Inf 4(4):567–575
Annunziata R, Garzelli A, Ballerini L, Mecocci A, Trucco E (2015) Leveraging multiscale hessian-based enhancement with a novel exudate inpainting technique for retinal vessel segmentation. IEEE J Biomed Health Inf 20(4):1129–1138
Marin D, Aquino A, Gegundez-Arias M, Bravo J (2011) A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans Med Imaging 30:146–158
Fraz M, Welikala R, Rudnicka A, Owen C, Strachan D, Barman S (2015) QUARTZ: quantitative analysis of retinal vessel topology and size—an automated system for quantification of retinal vessels morphology. Expert Syst Appl 42(20):7221–7234
Bao XR, Ge X, She LH, Zhang S (2015) Segmentation of retinal blood vessels based on cake filter. BioMed Res Int 2015:137024–137024
Kar S, Maity S (2016) Blood vessel extraction and optic disc removal using curvelet transform and kernel fuzzy c-means. Comput Biol Med 70:174–189
Emary E, Zawbaa H, Hassanien A, Parv B (2016) Multi-objective retinal vessel localization using flower pollination search algorithm with pattern search. Adv Data Anal Classif 11:1–7
Shehhi RA, Marpu P, Woon W (2016) An automatic cognitive graph-based segmentation for detection of blood vessels in retinal images. Mathe Probl Eng 2016:15. https://doi.org/10.1155/2016/7906165
Khan KB, Khaliq AA, Shahid M (2016) A morphological hessian based approach for retinal blood vessels segmentation and denoising using region based otsu thresholding. PLoS ONE 11(7):e0158996
Mendonca A, Campilho A (2006) Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans Med Imaging 25(9):1200–1213
Zana F, Jean-Claude K (2001) Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans Image Process 10(7):1010–1019
Ayala G, León T, Zapater V (2005) Different averages of a fuzzy set with an application to vessel segmentation. IEEE Trans Fuzzy Syst 13(3):384–393
Yang Y, Huang S, Rao N (2008) An automatic hybrid method for retinal blood vessel extraction. Int J Appl Math Comput Sci 18(3):399–407
Fraz M, Remagnino P, Hoppe A, Uyyanonvara B, Owen C, Rudnicka A, Barman S (2011) Retinal vessel extraction using first-order derivative of Gaussian and morphological processing. In: Advances in visual computing. Springer, Berlin Heidelberg, pp 410–420
Miri M, Mahloojifar A (2011) Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction. IEEE Trans Biomed Eng 58(5):1183–1192
Rossant F, Badellino M, Chavillon A, Bloch I, Paques M (2011) A morphological approach for vessel segmentation in eye fundus images, with quantitative evaluation. J Med Imaging Health Inf 1(1):42–49
Fraz M, Barman S, Remagnino P, Hoppe A, Basit A, Uyyanonvara B, Rudnicka A, Owen C (2012) An approach to localize the retinal blood vessels using bit planes and centerline detection. Comput Methods Programs Biomed 108(2):600–616
Fraz M, Basit A, Barman S (2013) Application of morphological bit planes in retinal blood vessel extraction. J Digit Imaging 26(2):274–286
Xu Y, Géraud T, Najman L (2013) Two applications of shape-based morphology: blood vessels segmentation and a generalization of constrained connectivity. In: Mathematical morphology and its applications to signal and image processing. Springer, Berlin Heidelberg, pp 390–401
Sigurðsson E, Valero S, Benediktsson J, Chanussot J, Talbot H, Stefánsson E (2014) Automatic retinal vessel extraction based on directional mathematical morphology and fuzzy classification. Pattern Recognit Lett 47:164–171
Imani E, Javidi M, Pourreza H (2015) Improvement of retinal blood vessel detection using morphological component analysis. Comput Methods Programs Biomed 118(3):263–279
Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M (1989) Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans Med Imaging 8(3):263–269
Zhou L, Rzeszotarski M, Singerman L, Chokreff J (1994) The detection and quantification of retinopathy using digital angiograms. IEEE Trans Med Imaging 13(4):619–626
Al-Rawi M, Qutaishat M, Arrar M (2007) An improved matched filter for blood vessel detection of digital retinal images. Comput Biol Med 37(2):262–267
Zhang L, Li Q, You J, Zhang D (2009) A modified matched filter with double-sided thresholding for screening proliferative diabetic retinopathy. IEEE Trans Inf Technol Biomed 13(4):528–534
Gang L, Chutatape O, Krishnan SM (2002) Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter. IEEE Trans Biomed Eng 49(2):168–172
Jiang X, Mojon D (2003) Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Trans Pattern Anal Mach Intell 25(1):131–137
Sukkaew L, Uyyanonvara B, Barman S, Fielder A, Cocker K (2007) Automatic extraction of the structure of the retinal blood vessel network of premature infants. J Med Assoc Thai 90(9):1780–1792
Cinsdikici M, Aydın D (2009) Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm. Comput Methods Programs Biomed 96(2):85–95
Amin M, Yan H (2011) High speed detection of retinal blood vessels in fundus image using phase congruency. Soft Comput 15(6):1217–1230
Kaba D, Salazar-Gonzalez A, Li Y, Liu X, Serag A (2013) Segmentation of retinal blood vessels using gaussian mixture models and expectation maximisation. In: Health Information Science. Springer, Berlin Heidelberg, pp 105–112
Chakraborti T, Jha D, Chowdhury A, Jiang X (2015) A self-adaptive matched filter for retinal blood vessel detection. Mach Vis Appl 26(1):55–68
Zhang J, Bekkers E, Abbasi S, Dashtbozorg B, Romeny BTH (2015) Robust and fast vessel segmentation via Gaussian derivatives in orientation scores. In: Image analysis and processing—ICIAP 2015. Springer, pp 537–547
Singh N, Srivastava R (2016) Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter. Comput Methods Programs Biomed 129:40–50
Fadzil MA, Izhar L, Venkatachalam P, Karunakar T (2007) Extraction and reconstruction of retinal vasculature. J Med Eng Technol 31(6):435–442
Palomera-Pérez M, Martinez-Perez M, Benítez-Pérez H, Ortega-Arjona J (2010) Parallel multiscale feature extraction and region growing: application in retinal blood vessel detection. IEEE Trans Inf Technol Biomed 14(2):500–506
Jiang H, He B, Fang D, Ma Z, Yang B, Zhang L (2013). A region growing vessel segmentation algorithm based on spectrum information. Comput Math Methods Med 2013:743870–743870
Zhao Y, Wang X, Wang X, Shih F (2014) Retinal vessels segmentation based on level set and region growing. Pattern Recognit 47(7):2437–2446
Dizdaroğlu B, Ataer-Cansizoglu E, Kalpathy-Cramer J, Keck K, Chiang M, Erdogmus D (2014) Structure-based level set method for automatic retinal vasculature segmentation. EURASIP J Image Video Process 2014(1):1–26
You S, Bas E, Erdogmus D, Kalpathy-Cramer J (2011) Principal curved based retinal vessel segmentation towards diagnosis of retinal diseases. In: Healthcare informatics, imaging and systems biology (HISB), 2011 first IEEE international conference. IEEE, pp 331–337
Panda R, Puhan NB, Panda G (2016). New binary Hausdorff symmetry measure based seeded region growing for retinal vessel segmentation. Biocybern Biomed Eng 36(1):119–129
Lázár I, Hajdu A (2015) Segmentation of retinal vessels by means of directional response vector similarity and region growing. Comput Biol Med 66:209–221
Staal J, Abràmoff M, Niemeijer M, Viergever M, Ginneken BV (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501–509
Soares J, Leandro J, Jr RC, Jelinek H, Cree M (2006) Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans Med Imaging 25(9):1214–1222
Anzalone A, Bizzarri F, Parodi M, Storace M (2008) A modular supervised algorithm for vessel segmentation in red-free retinal images. Comput Biol Med 38(8):913–922
Osareh A, Shadgar B (2009) Automatic blood vessel segmentation in color images of retina. Iran J Sci Technol 33(B2):191–206
Xu L, Luo S (2010) A novel method for blood vessel detection from retinal images. Biomed Eng Online 9(1):14
Lupaşcu C, Tegolo D, Trucco E (2010) FABC: retinal vessel segmentation using AdaBoost. IEEE Trans Inf Technol Biomed 14(5):1267–1274
You X, Peng Q, Yuan Y, Cheung Y, Lei J (2011) Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recognit 44(10):2314–2324
Varnousfaderani E, Yousefi S, Bowd C, Belghith A, Goldbaum M (2015) Vessel delineation in retinal images using leung-malik filters and two levels hierarchical learning. AMIA Annu Symp Proc 2015:1140 American Medical Informatics Association
Roychowdhury S, Koozekanani D, Parhi K (2015) Blood vessel segmentation of fundus images by major vessel extraction and subimage classification. IEEE J Biomed Health Inf 19(3):1118–1128
Orlando JI, Prokofyeva E, Blaschko MB (2017) A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE Trans Biomed Eng 64(1):16–27
Akita K, Kuga H (1982) A computer method of understanding ocular fundus images. Pattern Recognit 15(6):431–443
Sinthanayothin C, Boyce J, Cook H, Williamson T (1999) Automated localization of the optic disc, fovea, and retinal blood vessels from digital color fundus images. Br J Ophthalmol 83(8):902–910
Nekovei R, Sun Y (1995) Back-propagation network and its configuration for blood vessel detection in angiograms. IEEE Trans Neural Netw 6(1):64–72
Yao C, Chen H (2009) Automated retinal blood vessels segmentation based on simplified PCNN and fast 2D-Otsu algorithm. J Cent South Univ Technol 16:640–646
Lupaşcu C, Tegolo D (2010) Automatic unsupervised segmentation of retinal vessels using self-organizing maps and k-means clustering. In: Computational intelligence methods for bioinformatics and biostatistics. Springer, Berlin Heidelberg, pp 263–274
Vega R, Guevara E, FalconL, Sanchez-Ante G, Sossa H (2013) Blood vessel segmentation in retinal images using lattice neural networks. In: Advances in artificial intelligence and its applications. Springer, Berlin Heidelberg, pp 532–544
Vega R, Sanchez-Ante G, Falcon-Morales L, Sossa H, Guevara E (2015) Retinal vessel extraction using lattice neural networks with dendritic processing. Comput Biol Med 58:20–30
Sossa H, Guevara E (2014) Efficient training for dendrite morphological neural networks. Neurocomputing 131:132–142
Andersson T, Lathen G, Lenz R, Borga M (2013) Modified gradient search for level set based image segmentation. IEEE Trans Image Process 22(2):621–630
Anitha J, Hemanth D (2013) An efficient Kohonen-fuzzy neural network based abnormal retinal image classification system. Neural Netw World 23(2):149–167
Franklin S, Rajan S (2014) Retinal vessel segmentation employing ANN technique by Gabor and moment invariants-based features. Appl Soft Comput 22:94–100
Li Q, Feng B, Xie L, Liang P, Zhang H, Wang T (2016) A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans Med Imaging 35(1)109–118
Sironi A, Tekin B, Rigamonti R, Lepetit V, Fua P (2015) Learning separable filters. IEEE Trans Pattern Anal Mach Intell 37(1):94–106
Ceylan M, Yasar H (2016) A novel approach for automatic blood vessel extraction in retinal images: complex ripplet-I transform and complex valued artificial neural network. Turk J Electr Eng Comput Sci 24(4):3212–3227
Liskowski P, Krawiec K (2016) Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging 35(11):2369–2380
Fraz M, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka A, Owen C, Barman S (2012) Ensemble classification system applied for retinal vessel segmentation on child images containing various vessel profiles. In: Image analysis and recognition. Springer, Berlin Heidelberg, pp 380–389
Fraz M, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka A, Owen C, Barman S (2012) An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans Biomed Eng 59(9):2538–2548
Fraz M, Rudnicka A, Owen C, Barman S (2014) Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification. Int J Comput Assist Radiol Surg 9(5):795–811
Wang S, Yin Y, Cao G, Wei B, Zheng Y, Yang G (2015) Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing 149:708–717
Welikala R, Fraz M, Foster P, Whincup P, Rudnicka A, Owen C, Strachan D, Barman S (2016) Automated retinal image quality assessment on the UK Biobank dataset for epidemiological studies. Comput Biol Med 71:67–76
Zhu C, Zou B, Xiang Y, Cui J, Wu H (2016) An ensemble retinal vessel segmentation based on supervised learning in fundus images. Chin J Electron 25(3):503–511
Liu I, Sun Y (1993) Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme. IEEE Trans Med Imaging 12:334–341
Chutatape O, Liu Z, Krishnan SM (1998) Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters. In: Engineering in medicine and biology society, 1998. Proceedings of the 20th annual international conference of the IEEE, vol 20, no 6, pp 3144–3149
Tolias Y, Panas S (1998) A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Trans Med Imaging 17(2):263–273
Can A, Shen H, Turner J, Tanenbaum H, Roysam B (1999) Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms. IEEE Trans Inf Technol Biomed 3(2):125–138
Lalonde M, Gagnon L, Boucher M (2000) Non-recursive paired tracking for vessel extraction from retinal images. In: Vision interface, pp 61–68
Quek F, Kirbas C (2001) Vessel extraction in medical images by wave-propagation and traceback. IEEE Trans Med Imaging 20(2):117–131
Delibasis K, Kechriniotis A, Tsonos C, Assimakis N (2010) Automatic model-based tracing algorithm for vessel segmentation and diameter estimation. Comput Methods Programs Biomed 100(2):108–122
Xu X, Niemeijer M, Song Q, Sonka M, Garvin M, Reinhardt J, Abràmoff M (2011) Vessel boundary delineation on fundus images using graph-based approach. IEEE Trans Med Imaging 30(6):1184–1191
Huang Y, Zhang J, Huang Y (2012) An automated computational framework for retinal vascular network labeling and branching order analysis. Microvasc Res 84(2):169–177
Yin Y, Adel M, Bourennane S (2012) Retinal vessel segmentation using a probabilistic tracking method. Pattern Recognit 45(4):1235–1244
Nayebifar B, Moghaddam H (2013) A novel method for retinal vessel tracking using particle filters. Comput Biol Med 43(5):541–548
Fraz M, Remagnino P, Hoppe A, Rudnicka A, Owen C, Whincup P, Barman S (2013) 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
Yin Y, Adel M, Bourennane S (2013) Automatic segmentation and measurement of vasculature in retinal fundus images using probabilistic formulation. Computational and mathematical methods in medicine 2013:260410–260410
Vázquez S, Cancela B, Barreira N, Penedo M, Rodríguez-Blanco M, Seijo M, Tuero GD, Barceló M, Saez M (2013) Improving retinal artery and vein classification by means of a minimal path approach. Mach Vis Appl 24(5):919–930
Bekkers E, Duits R, Berendschot T, Romeny BTH (2014) A multi-orientation analysis approach to retinal vessel tracking. J Math Imaging Vis 49(3):583–610
De J, Ma T, Li H, Dash M, Li C (2013) Automated tracing of retinal blood vessels using graphical models. In: Image analysis. Springer, Berlin, pp 277–289
De J, Li H, Cheng L (2014) Tracing retinal vessel trees by transductive inference. BMC Bioinform 15(1):20
Poletti E, Ruggeri A (2014) Graph search retinal vessel tracking. In: Ophthalmological imaging and applications, pp 97–115
Zhang J, Li H, Nie Q, Cheng L (2014) A retinal vessel boundary tracking method based on Bayesian theory and multi-scale line detection. Comput Med Imaging Graph 38(6):517–525
Cheng L, De J, Zhang X, Lin F, Li H (2014) Tracing retinal blood vessels by matrix-forest theorem of directed graphs. In: Medical image computing and computer-assisted intervention–MICCAI, Springer, pp 626–633
Chen D, Cohen L (2015) Piecewise geodesics for vessel centerline extraction and boundary delineation with application to retina segmentation. In: Scale space and variational methods in computer vision, pp 270–281
Vermeer K, Vos F, Lemij H, Vossepoel A (2004) A model based method for retinal blood vessel detection. Comput Biol Med 34(3):209–219
Huber P (1965) A robust version of the probability ratio test. Ann Math Stat 36(6):1753–1758
Field C, Smith B (1994) Robust estimation: a weighted maximum likelihood approach. Int Stat Rev 405–424
Ronchetti E (1985) Robust model selection in regression. Stat Probab Lett 3(3):21–23
Mahadevan V, Narasimha-Iyer H, Roysam B, Tanenbaum H (2004) Robust model-based vasculature detection in noisy biomedical images. IEEE Trans Inf Technol Biomed 8(3):360–376
Narasimha-Iyer H, Mahadevan V, Beach J, Roysam B (2008) 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
Alonso-Montes C, Vilariño D, Penedo M (2005) On the automatic 2D retinal vessel extraction. In: Pattern recognition and image analysis. Springer, Berlin, pp 165–173
Perfetti R, Ricci E, Casali D, Costantini G (2007) Cellular neural networks with virtual template expansion for retinal vessel segmentation. IEEE Trans Circuits Syst II Express Briefs 54(2):141–145
Wang L, Bhalerao A, Wilson R (2007) Analysis of retinal vasculature using a multi-resolution hermite model. IEEE Trans Med Imaging 26(2):137–152
Narasimha-Iyer H, Beach J, Khoobehi B, Roysam B (2007) Automatic identification of retinal arteries and veins from dual-wavelength images using structural and functional features. IEEE Trans Biomed Eng 54(8):1427–1435
Lam B, Yan H (2008) A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields. IEEE Trans Med Imaging 27(2):237–246
Alonso-Montes C, Vilarino D, Dudek P, Penedo M (2008) Fast retinal vessel tree extraction: a pixel parallel approach. Int J Circuit Theory Appl 36(5–6):641–651
Alonso-Montes C, Vilarino D, Penedo M (2005) CNN-based automatic retinal vascular tree extraction. In: Cellular neural networks and their applications, 2005 9th International Workshop. IEEE, pp 61–64
Dudek P, Carey S (2006) General-purpose 128/spl times/128 SIMD processor array with integrated image sensor. Electron Lett 42(12):678–679
Vilariño D, Rekeczky C (2004) Implementation of a pixel-level snake algorithm on a CNNUM-based chip set architecture. IEEE Trans Circuits Syst I Regul Pap 51(5):885–891
Lam B, Gao Y, Liew A (2010) General retinal vessel segmentation using regularization-based multiconcavity modeling. IEEE Trans Med Imaging 29(7):1369–1381
Gao X, Bharath A, Stanton A, Hughes A, Chapman N, Thom S (2001) A method of vessel tracking for vessel diameter measurement on retinal images. In: Image processing, proceedings 2001 international conference. IEEE, vol 2, pp 881–884
Zhu T (2010) Fourier cross-sectional profile for vessel detection on retinal images. Comput Med Imaging Graph 34(3):203–212
Kovesi P (2003) Phase congruency detects corners and edges. In: The Australian pattern recognition society conference: DICTA
Kovács G, Hajdu A (2016) A self-calibrating approach for the segmentation of retinal vessels by template matching and contour reconstruction. Med Image Anal 29:24–46
Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1(4):321–331
McInerney T, Terzopoulos D (2000) T-snakes: topology adaptive snakes. Med Image Anal 4(2):73–91
McInerney T, Hamarneh G, Shenton M, Terzopoulos D (2002) Deformable organisms for automatic medical image analysis. Med Image Anal 6(3):251–266
Nain D, Yezzi A, Turk G (2004) Vessel segmentation using a shape driven flow. In: Medical image computing and computer-assisted intervention–MICCAI. Springer, Berlin, pp 51–59
Espona L, Carreira M, Ortega M, Penedo M (2007) A snake for retinal vessel segmentation. In: Pattern recognition and image analysis. Springer, Berlin, pp 178–185
Al-Diri B, Hunter A, Steel D (2009) An active contour model for segmenting and measuring retinal vessels. IEEE Trans Med Imaging 28(9):1488–1497
Chan T, Vese L (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277
Sum K, Cheung P (2008) Vessel extraction under non-uniform illumination: a level set approach. IEEE Trans Biomed Eng 55(1):358–360
Zhang Y, Hsu W, Lee M (2009) Detection of retinal blood vessels based on nonlinear projections. J Signal Process Syst 55(1–3):103–112
Oloumi F, Rangayyan R, Ells A (2012) Parabolic modeling of the major temporal arcade in retinal fundus images. IEEE Trans Instrum Meas 61(7):1825–1838
Rouchdy Y, Cohen L (2013) Geodesic voting methods: overview, extensions and application to blood vessel segmentation. Comput Methods Biomech Biomed Eng Imaging Vis 1(2):79–88
Guo Z, Lin P, Ji G, Wang Y (2014) Retinal vessel segmentation using a finite element based binary level set method. Inverse Probl Imaging 8(2):459–473
Lermé N, Rossant F, Bloch I, Paques M, Koch E (2014) Coupled parallel snakes for segmenting healthy and pathological retinal arteries in adaptive optics images. In: Image analysis and recognition. Springer, pp 311–320
Zhao Y, Rada L, Chen K, Harding S (2015) 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
Wang L, Zhang H, He K, Chang Y, Yang X (2015) Active contours driven by multi-feature gaussian distribution fitting energy with application to vessel segmentation. PLoS ONE 10(11):e0143105
Rad AE, Rahim MSM, Kolivand H, Amin IBM (2017) Morphological region-based initial contour algorithm for level set methods in image segmentation. Multimed Tools Appl 76(2):2185–2201
Oliveira W, Teixeira J, Ren T, Cavalcanti G, Sijbers J (2016) Unsupervised retinal vessel segmentation using combined filters. PLoS ONE 11(2):e0149943
Nieto A, Brea V, Vilariňo D (2009) FPGA-accelerated retinal vessel-tree extraction. In: Field programmable logic and applications, FPL 2009. International conference, IEEE, pp 485–488
Krause M, Alles RM, Burgeth B, Weickert J (2016) Fast retinal vessel analysis. J Real Time Image Process 11(2):413–422
Koukounis D, Ttofis C, Papadopoulos A, Theocharides T (2014) A high performance hardware architecture for portable, low-power retinal vessel segmentation. Integr VLSI J 47(3):377–386
Argüello F, Vilariño DL, Heras DB, Nieto A (2018) GPU-based segmentation of retinal blood vessels. J Real Time Image Process 14(4):773–782
Villalobos-Castaldi F, Felipe-Riverón E, Sánchez-Fernández L (2010) A fast, efficient and automated method to extract vessels from fundus images. J Vis 13(3):263–270
Condurache A, Mertins A (2012) Segmentation of retinal vessels with a hysteresis binary-classification paradigm. Comput Med Imaging Graph 36(4):325–335
Mudassar A A, Butt S (2013) Extraction of blood vessels in retinal images using four different techniques. J Med Eng 2013:408120–408120
Salazar-Gonzalez A, Kaba D, Li Y, Liu X (2014) Segmentation of blood vessels and optic disc in retinal images. IEEE J Biomed Health Inf 18(6):1874–1886
Jiang K, Zhou Z, Geng X, Zhang X, Tang L, Wu H, Dong J (2015) Isotropic undecimated wavelet transform fuzzy algorithm for retinal blood vessel segmentation. J Med Imaging Health Inf 5(7):1524–1527
Dai P, Luo H, Sheng H, Zhao Y, Li L, Wu J, Zhao Y, Suzuki K (2015) A new approach to segment both main and peripheral retinal vessels based on gray-voting and gaussian mixture model. PLoS ONE 10(6):e0127748
Fumero F, Alayón S, Sanchez J, Sigut J, Gonzalez-Hernandez M (2011) RIM-ONE: an open retinal image database for optic nerve evaluation. In: Computer-based medical systems (CBMS), 2011 24th international symposium, IEEE, pp 1–6
Niemeijer M, Staal J, Ginneken B, Loog M, Abramoff M (2004) DRIVE: digital retinal images for vessel extraction. http://www.isi.uu.nl/Research/Databases/DRIVE
MESSIDOR: Methods for evaluating segmentation and indexing techniques dedicated to retinal ophthalmology (2004) http://messidor.crihan.fr/index-en.php
ARIA online, retinal image archive (2006) http://www.eyecharity.com/aria_online.html
Kauppi T, Kalesnykiene V, Kamarainen JK, Lensu L, Sorri I, Raninen A, Voutilainen R, Uusitalo H, K¨alvi¨ainen H, Pietil J (2007) DIARETDB1 diabetic retinopathy database and evaluation protocol. Proc Med Image Underst Anal MIUA 1:3–7
IMAGERET-optimal detection and decision-support diagnosis of diabetic retinopathy. http://www.it.lut.fi/project/imageret/
Al-Diri B, Hunter A, Steel D, Habib M, Hudaib T, Berry S (2008) REVIEW-a reference data set for retinal vessel profiles. In: Engineering in medicine and biology society. EMBS 2008, 30th annual international conference of the IEEE, pp 2262–2265
Carmona E, Rincón M, García-Feijoó J, Martínez-de-la-Casa J (2008) Identification of the optic nerve head with genetic algorithms. Artif Intell Med 43(3):243–259
García-Feijoo J, Martínez-de-la-Casa JM, Carmona E, Rincón M, Mayoral M (2008) DRIONS-DB. http://www.ia.uned.es/~ejcarmona/DRIONS-DB.html
Niemeijer M, Ginneken BV, Cree M, Mizutani A, Quellec G, Sánchez C, Zhang B, Hornero R, Lamard M, Muramatsu C, Wu X (2010) Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs. IEEE Trans Med Imaging 29(1):185–195
Budai A, Hornegger J, Michelson G (2009) Multiscale approach for blood vessel segmentation on retinal fundus images. Invest Ophthalmol Vis Sci 50(13):325
The VICAVR database (2010) http://www.varpa.es/vicavr.html
Giancardo L, Meriaudeau F, Karnowski T, Li Y, Garg S, Tobin K, Chaum E (2012) Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Med Image Anal 16(1):216–226
Prentasic P, Loncaric S, Vatavuk Z, Bencic G, Subasic M, Petkovic T, Dujmovic L, Malenica-Ravlic M, Budimlija N, Tadic R (2013) Diabetic retinopathy image database (DRiDB): a new database for diabetic retinopathy screening programs research. In: Image and signal processing and analysis (ISPA), 2013 8th international symposium, pp 711–716
Shahbeig S (2013) Automatic and quick blood vessels extraction algorithm in retinal images. IET Image Proc 7(4):392–400
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Khan, K.B., Khaliq, A.A., Jalil, A. et al. A review of retinal blood vessels extraction techniques: challenges, taxonomy, and future trends. Pattern Anal Applic 22, 767–802 (2019). https://doi.org/10.1007/s10044-018-0754-8
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DOI: https://doi.org/10.1007/s10044-018-0754-8