21.5.3 Retinal Images, Angiography, Blood Vessels in the Eye

Chapter Contents (Back)
Retinal Images. Retinal Vessels. Blood Vessels. Retinal Angiogrphy. Vessels. Eye. Medical, Applications.
See also Retinal Microaneurysms, Detection.
See also Diabetic Retinopathy, Retinal Analysis Application.

Ibanez, M.V., Simo, A.,
Bayesian detection of the fovea in eye fundus angiographies,
PRL(20), No. 2, February 1999, pp. 229-240. BibRef 9902

Simó, A., de Ves, E.,
Segmentation of Macular Fluorescein Angiographies. A Statistical Approach,
PR(34), No. 4, April 2001, pp. 795-809.
Elsevier DOI 0101
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Pedersen, L., Grunkin, M., Ersbøll, B.K., Madsen, K., Larsen, M., Christoffersen, N., Skands, U.,
Quantitative Measurement of Changes in Retinal Vessel Diameter in Ocular Fundus Images,
PRL(21), No. 13-14, December 2000, pp. 1215-1223. 0011
BibRef
Earlier: SCIA99(Biological Applications II). BibRef

Hoover, A.D., Kouznetsova, V., Goldbaum, M.,
Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,
MedImg(19), No. 3, March 2000, pp. 203-210.
IEEE Top Reference. 0110
BibRef

Hoover, A.D., Goldbaum, M.,
Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels,
MedImg(22), No. 8, August 2003, pp. 951-958.
IEEE Abstract. 0308
BibRef

Bouaoune, Y., Assogba, M.K., Nunes, J.C., Bunel, P.,
Spatio-temporal characterization of vessel segments applied to retinal angiographic images,
PRL(24), No. 1-3, January 2003, pp. 607-615.
Elsevier DOI 0211
BibRef

Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Kennedy, R.L.,
Measurement of Retinal Vessel Widths from Fundus Images Based on 2-D Modeling,
MedImg(23), No. 10, October 2004, pp. 1196-1204.
IEEE Abstract. 0410

See also Optic Nerve Head Segmentation. BibRef

Staal, J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.,
Ridge-based vessel segmentation in color images of the retina,
MedImg(23), No. 4, April 2004, pp. 501-509.
IEEE Abstract. 0406
BibRef

Grisan, E., Foracchia, M., Ruggeri, A.,
A Novel Method for the Automatic Grading of Retinal Vessel Tortuosity,
MedImg(27), No. 3, March 2008, pp. 310-319.
IEEE DOI 0803
BibRef

Stosic, T., Stosic, B.D.,
Multifractal Analysis of Human Retinal Vessels,
MedImg(25), No. 8, August 2006, pp. 1101-1107.
IEEE DOI 0608
BibRef

Soares, J.V.B., Leandro, J.J.G., Cesar, Jr., R.M., Jelinek, H.F., Cree, M.J.,
Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification,
MedImg(25), No. 9, September 2006, pp. 1214-1222.
IEEE DOI 0609
BibRef

Sofka, M., Stewart, C.V.[Charles V.],
Retinal Vessel Centerline Extraction Using Multiscale Matched Filters, Confidence and Edge Measures,
MedImg(25), No. 12, December 2006, pp. 1531-1546.
IEEE DOI 0701
BibRef
And: Erratum: MedImg(26), No. 1, January 2007, pp. 133-133.
IEEE DOI 0701
BibRef

Stewart, C.V.[Charles V.],
Computer Vision Algorithms for Retinal Image Analysis: Current Results and Future Directions,
CVBIA05(31-50).
Springer DOI 0601
BibRef

Wang, L.[Li], Bhalerao, A.H., Wilson, R.,
Analysis of Retinal Vasculature Using a Multiresolution Hermite Model,
MedImg(26), No. 2, February 2007, pp. 137-152.
IEEE DOI 0702
BibRef
Earlier:
Robust modelling of local image structures and its application to medical imagery,
ICPR04(III: 534-537).
IEEE DOI 0409
BibRef

Thonnes, E., Bhalerao, A.H., Kendall, W., Wilson, R.,
A bayesian approach to inferring vascular tree structure from 2d imagery,
ICIP02(II: 937-940).
IEEE DOI 0210
BibRef

Ricci, E., Perfetti, R.,
Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification,
MedImg(26), No. 10, October 2007, pp. 1357-1365.
IEEE DOI 0711
BibRef

Youssif, A.A.H.A.R., Ghalwash, A.Z., Ghoneim, A.A.S.A.R.,
Optic Disc Detection From Normalized Digital Fundus Images by Means of a Vessels' Direction Matched Filter,
MedImg(27), No. 1, January 2008, pp. 11-18.
IEEE DOI 0712
BibRef

Adjeroh, D.A.[Donald A.], Kandaswamy, U.[Umasankar], Odom, J.V.[J. Vernon],
Texton-based segmentation of retinal vessels,
JOSA-A(24), No. 5, May 2007, pp. 1384-1393.
WWW Link. 0801
BibRef

Miura, M.[Masahiro], Elsner, A.E.[Ann E.], Cheney, M.C.[Michael C.], Usui, M.[Masahiko], Iwasaki, T.[Takuya],
Imaging polarimetry and retinal blood vessel quantification at the epiretinal membrane,
JOSA-A(24), No. 5, May 2007, pp. 1431-1437.
WWW Link. 0801
BibRef

Rothaus, K.[Kai], Jiang, X.Y.[Xiao-Yi], Rhiem, P.[Paul],
Separation of the retinal vascular graph in arteries and veins based upon structural knowledge,
IVC(27), No. 7, 4 June 2009, pp. 864-875.
Elsevier DOI 0904
BibRef
Earlier: A1, A3, A2:
Separation of the Retinal Vascular Graph in Arteries and Veins,
GbRPR07(251-262).
Springer DOI 0706
Retinal vascular graph; Artery; Vein; Constraint satisfaction problem; Constraint propagation
See also Structural Performance Evaluation of Curvilinear Structure Detection Algorithms with Application to Retinal Vessel Segmentation. BibRef

Jiang, X.Y.[Xiao-Yi], Lambers, M.[Martin], Bunke, H.[Horst],
Structural Performance Evaluation of Curvilinear Structure Detection Algorithms with Application to Retinal Vessel Segmentation,
PRL(33), No. 15, 1 November 2012, pp. 2048-2056.
Elsevier DOI 1210
BibRef
Earlier:
Structure-Based Evaluation Methodology for Curvilinear Structure Detection Algorithms,
GbRPR11(305-314).
Springer DOI 1105
Medical images. Not pixel level ground truth, but structure based.
See also Separation of the retinal vascular graph in arteries and veins based upon structural knowledge. Performance evaluation; Curvilinear structure; Vessel network; Airway tree; Graph matching BibRef

Jiang, X.Y.[Xiao-Yi], Mojon, D.,
Supervised evaluation methodology for curvilinear structure detection algorithms,
ICPR02(I: 103-106).
IEEE DOI 0211
BibRef

Al-Diri, B., Hunter, A., Steel, D.,
An Active Contour Model for Segmenting and Measuring Retinal Vessels,
MedImg(28), No. 9, September 2009, pp. 1488-1497.
IEEE DOI 0909
BibRef

Ng, J., Clay, S.T., Barman, S.A., Fielder, A.R., Moseley, M.J., Parker, K.H., Paterson, C.,
Maximum likelihood estimation of vessel parameters from scale space analysis,
IVC(28), No. 1, Januray 2010, pp. 55-63.
Elsevier DOI 1001
Scale space; Maximum likelihood; Retinal blood vessels; Automatic segmentation BibRef

Jlassi, H.[Hejer], Hamrouni, K.[Kamel],
Detection of Blood Vessels in Retinal Images,
IJIG(10), No. 1, January 2010, pp. 57-72.
DOI Link 1003
BibRef

Lam, B.S.Y., Gao, Y., Liew, A.W.C.,
General Retinal Vessel Segmentation Using Regularization-Based Multiconcavity Modeling,
MedImg(29), No. 7, July 2010, pp. 1369-1381.
IEEE DOI 1007
BibRef

Marin, D., Aquino, A., Gegundez-Arias, M.E., Bravo, J.M.,
A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features,
MedImg(30), No. 1, January 2011, pp. 146-158.
IEEE DOI 1101
BibRef

Gegundez-Arias, M.E., Aquino, A., Bravo, J.M., Marin, D.,
A Function for Quality Evaluation of Retinal Vessel Segmentations,
MedImg(31), No. 2, February 2012, pp. 231-239.
IEEE DOI 1202
BibRef

Azemin, M.Z.C., Kumar, D.K., Wong, T.Y., Kawasaki, R., Mitchell, P., Wang, J.J.,
Robust Methodology for Fractal Analysis of the Retinal Vasculature,
MedImg(30), No. 2, February 2011, pp. 243-250.
IEEE DOI 1102
BibRef

Goatman, K.A., Fleming, A.D., Philip, S., Williams, G.J., Olson, J.A., Sharp, P.F.,
Detection of New Vessels on the Optic Disc Using Retinal Photographs,
MedImg(30), No. 4, April 2011, pp. 972-979.
IEEE DOI 1104
BibRef

You, X.G.[Xin-Ge], Peng, Q.[Qinmu], Yuan, Y.[Yuan], Cheung, Y.M.[Yiu-Ming], Lei, J.J.[Jia-Jia],
Segmentation of retinal blood vessels using the radial projection and semi-supervised approach,
PR(44), No. 10-11, October-November 2011, pp. 2314-2324.
Elsevier DOI 1101
Radial projection; Retinal images; Steerable complex wavelet BibRef

Peng, Q.[Qinmu], You, X.G.[Xin-Ge], Zhou, L.[Long], Cheung, Y.M.[Yiu-Ming],
Retinal Blood Vessels Segmentation Using the Radial Projection and Supervised Classification,
ICPR10(1489-1492).
IEEE DOI 1008
BibRef

Xu, X., Niemeijer, M., Song, Q., Sonka, M., Garvin, M.K., Reinhardt, J.M., Abramoff, M.D.,
Vessel Boundary Delineation on Fundus Images Using Graph-Based Approach,
MedImg(30), No. 6, June 2011, pp. 1184-1191.
IEEE DOI 1101

See also Intraretinal Layer Segmentation of Macular Optical Coherence Tomography Images Using Optimal 3-D Graph Search. BibRef

Yin, Y.[Yi], Adel, M.[Mouloud], Bourennane, S.[Salah],
Retinal vessel segmentation using a probabilistic tracking method,
PR(45), No. 4, April 2012, pp. 1235-1244.
Elsevier DOI 1112
Retinal image; Edge detection; Vessel tracking; Bayesian segmentation BibRef

Yin, Y.[Yi], Adel, M.[Mouloud], Guillaume, M.[Mireille], Bourennane, S.[Salah],
A probabilistic based method for tracking vessels in retinal images,
ICIP10(4081-4084).
IEEE DOI 1009
BibRef

Perez-Rovira, A., Cabido, R., Trucco, E., McKenna, S.J., Hubschman, J.P.,
RERBEE: Robust Efficient Registration via Bifurcations and Elongated Elements Applied to Retinal Fluorescein Angiogram Sequences,
MedImg(31), No. 1, January 2012, pp. 140-150.
IEEE DOI 1201
BibRef

Wang, Y.F.[Yang-Fan], Ji, G.R.[Guang-Rong], Lin, P.[Ping], Trucco, E.[Emanuele],
Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition,
PR(46), No. 8, August 2013, pp. 2117-2133.
Elsevier DOI 1304
Vessel detection; Retinal images; Segmentation; Matched filter; Multiwavelet; Multiscale hierarchical decomposition BibRef

Lupascu, C.A.[Carmen Alina], Tegolo, D.[Domenico], Trucco, E.[Emanuele],
A Comparative Study on Feature Selection for Retinal Vessel Segmentation Using FABC,
CAIP09(655-662).
Springer DOI 0909
BibRef

Azegrouz, H., Trucco, E.,
Max-Min Central Vein Detection in Retinal Fundus Images,
ICIP06(1925-1928).
IEEE DOI 0610
BibRef

Esmaeili, M.[Mahdad], Rabbani, H.[Hossein], Mehri Dehnavi, A.[Alireza], Dehghani, A.[Alireza],
Automatic detection of exudates and optic disk in retinal images using curvelet transform,
IET-IPR(6), No. 7, 2012, pp. 1005-1013.
DOI Link 1211
BibRef
Earlier:
A new curvelet transform based method for extraction of red lesions in digital color retinal images,
ICIP10(4093-4096).
IEEE DOI 1009
BibRef
Earlier:
Extraction of retinal blood vessels by curvelet transform,
ICIP09(3353-3356).
IEEE DOI 0911
BibRef

Nguyen, U.T.V.[Uyen T.V.], Bhuiyan, A.[Alauddin], Park, L.A.F.[Laurence A.F.], Ramamohanarao, K.[Kotagiri],
An effective retinal blood vessel segmentation method using multi-scale line detection,
PR(46), No. 3, March 2013, pp. 703-715.
Elsevier DOI 1212
Retinal image; Vessel extraction; Line detector; Central reflex BibRef

Nguyen, U.T.V.[Uyen T. V.], Ramamohanarao, K.[Kotagiri], Park, L.A.F.[Laurence A. F.], Wang, L.[Liang], Bhuiyan, A.[Alauddin],
A Quantitative Measure for Retinal Blood Vessel Segmentation Evaluation,
IJCVSP(1), No. 1, 2012, pp. xx-yy.
WWW Link. 1212
BibRef
Earlier: A1, A5, A2, A3, Only:
An Effective Supervised Framework for Retinal Blood Vessel Segmentation Using Local Standardisation and Bagging,
MLMI11(117-125).
Springer DOI 1109
BibRef

Bhuiyan, A.[Alauddin], Nath, B.[Baikunth], Ramamohanarao, K.[Kotagiri],
Detection and Classification of Bifurcation and Branch Points on Retinal Vascular Network,
DICTA12(1-8).
IEEE DOI 1303
BibRef

Bhuiyan, A.[Alauddin], Kawasaki, R.[Ryo], Lamoureux, E.[Ecosse], Wong, T.Y.[Tien Y.], Ramamohanarao, K.[Kotagiri],
Vessel Segmentation from Color Retinal Images with Varying Contrast and Central Reflex Properties,
DICTA10(184-189).
IEEE DOI 1012
BibRef

Bhuiyan, A.[Alauddin], Nath, B.[Baikunth], Chua, J.J.[Joselito J.], Ramamohanarao, K.[Kotagiri],
Blood Vessel Segmentation from Color Retinal Images using Unsupervised Texture Classification,
ICIP07(V: 521-524).
IEEE DOI 0709
BibRef

Azzopardi, G.[George], Azzopardi, N.[Nicolai],
Trainable COSFIRE Filters for Keypoint Detection and Pattern Recognition,
PAMI(35), No. 2, February 2013, pp. 490-503.
IEEE DOI 1301
BibRef
Earlier:
Detection of Retinal Vascular Bifurcations by Rotation- and Scale-Invariant COSFIRE Filters,
ICIAR12(II: 363-371).
Springer DOI 1206
COSFIRE: Combination Of Shifted FIlter REsponses. Selective for a local contour pattern specified by an example. BibRef

Azzopardi, G.[George], Petkov, N.[Nicolai],
Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters,
PRL(34), No. 8, June 2013, pp. 922-933.
Elsevier DOI 1305
BibRef
Earlier:
Detection of Retinal Vascular Bifurcations by Trainable V4-Like Filters,
CAIP11(I: 451-459).
Springer DOI 1109
Gabor filters; Keypoint detection; Retinal fundus; Trainable filters; Vascular bifurcation BibRef

Guo, J.P.[Jia-Pan], Shi, C.Y.[Chen-Yu], Azzopardi, G.[George], Petkov, N.[Nicolai],
Inhibition-augmented trainable COSFIRE filters for keypoint detection and object recognition,
MVA(27), No. 8, November 2016, pp. 1197-1211.
Springer DOI 1612
BibRef

Gecer, B.[Baris], Azzopardi, G.[George], Petkov, N.[Nicolai],
Color-blob-based COSFIRE filters for object recognition,
IVC(57), No. 1, 2017, pp. 165-174.
Elsevier DOI 1702
BibRef
Earlier: A2, A3:
A Shape Descriptor Based on Trainable COSFIRE Filters for the Recognition of Handwritten Digits,
CAIP13(II:9-16).
Springer DOI 1311
Object recognition BibRef

Turior, R.[Rashmi], Onkaew, D.[Danu], Uyyanonvara, B.[Bunyarit],
PCA-Based Retinal Vessel Tortuosity Quantification,
IEICE(E96-D), No. 2, February 2013, pp. 329-339.
WWW Link. 1301
BibRef

Tang, L., Niemeijer, M., Reinhardt, J.M., Garvin, M.K., Abramoff, M.D.,
Splat Feature Classification With Application to Retinal Hemorrhage Detection in Fundus Images,
MedImg(32), No. 2, February 2013, pp. 364-375.
IEEE DOI 1301
BibRef

Vázquez, S.G., Cancela, B., Barreira, N., Penedo, M.G., Rodríguez-Blanco, M., Pena Seijo, M., Coll de Tuero, G., Barceló, M.A., Saez, M.,
Improving retinal artery and vein classification by means of a minimal path approach,
MVA(24), No. 5, July 2013, pp. 919-930.
Springer DOI 1306
BibRef

Odstrcilik, J., Kolar, R., Budai, A., Hornegger, J., Jan, J., Gazarek, J., Kubena, T., Cernosek, P., Svoboda, O., Angelopoulou, E.,
Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database,
IET-IPR(7), No. 4, 2013, pp. 373-383.
DOI Link 1307
BibRef

Sulai, Y.N.[Yusufu N.], Scoles, D.[Drew], Harvey, Z.[Zachary], Dubra, A.[Alfredo],
Visualization of retinal vascular structure and perfusion with a nonconfocal adaptive optics scanning light ophthalmoscope,
JOSA-A(31), No. 3, March 2014, pp. 569-579.
DOI Link 1403
Ophthalmic optics and devices BibRef

Zhao, Y.Q.[Yu Qian], Wang, X.H.[Xiao Hong], Wang, X.F.[Xiao Fang], Shih, F.Y.[Frank Y.],
Retinal vessels segmentation based on level set and region growing,
PR(47), No. 7, 2014, pp. 2437-2446.
Elsevier DOI 1404
Retinal vessel segmentation BibRef

Bekkers, E.[Erik], Duits, R.[Remco], Berendschot, T.[Tos], ter Haar Romeny, B.M.[Bart M.],
A Multi-Orientation Analysis Approach to Retinal Vessel Tracking,
JMIV(49), No. 3, July 2014, pp. 583-610.
Springer DOI 1407
BibRef

Dizdarolu, B.[Bekir], Ataer-Cansizoglu, E.[Esra], Kalpathy-Cramer, J.[Jayashree], Keck, K.[Katie], Chiang, M.[Michael], Erdogmus, D.[Deniz],
Structure-based level set method for automatic retinal vasculature segmentation,
JIVP(2014), No. 1, 2014, pp. 39.
DOI Link 1408
BibRef

Sigurðsson, E.M.[Eysteinn Már], Valero, S.[Silvia], Benediktsson, J.A.[Jón Atli], Chanussot, J.[Jocelyn], Talbot, H.[Hugues], Stefánsson, E.[Einar],
Automatic retinal vessel extraction based on directional mathematical morphology and fuzzy classification,
PRL(47), No. 1, 2014, pp. 164-171.
Elsevier DOI 1408
Fundus image BibRef

Cheng, E.[Erkang], Du, L.[Liang], Wu, Y.[Yi], Zhu, Y.J.[Ying J.], Megalooikonomou, V.[Vasileios], Ling, H.B.[Hai-Bin],
Discriminative vessel segmentation in retinal images by fusing context-aware hybrid features,
MVA(25), No. 7, October 2014, pp. 1779-1792.
Springer DOI 1410
BibRef

Gavet, Y.[Yann], Fernandes, M.[Mathieu], Debayle, J.[Johan], Pinoli, J.C.[Jean-Charles],
Dissimilarity criteria and their comparison for quantitative evaluation of image segmentation: application to human retina vessels,
MVA(25), No. 8, November 2014, pp. 1953-1966.
Springer DOI 1411
BibRef

Chakraborti, T.[Tapabrata], Jha, D.K.[Dhiraj K.], Chowdhury, A.S.[Ananda S.], Jiang, X.Y.[Xiao-Yi],
A self-adaptive matched filter for retinal blood vessel detection,
MVA(26), No. 1, January 2015, pp. 55-68.
Springer DOI 1503
BibRef

Paripurana, S.[Sukritta], Chiracharit, W.[Werapon], Chamnongthai, K.[Kosin], Saito, H.[Hideo],
Extraction of Blood Vessels in Retinal Images Using Resampling High-Order Background Estimation,
IEICE(E98-D), No. 3, March 2015, pp. 692-703.
WWW Link. 1504
BibRef

Hassanien, A.E.[Aboul Ella], Emary, E., Zawbaa, H.M.[Hossam M.],
Retinal blood vessel localization approach based on bee colony swarm optimization, fuzzy c-means and pattern search,
JVCIR(31), No. 1, 2015, pp. 186-196.
Elsevier DOI 1508
Retinal blood vessel BibRef

Estrada, R., Allingham, M.J., Mettu, P.S., Cousins, S.W., Tomasi, C., Farsiu, S.,
Retinal Artery-Vein Classification via Topology Estimation,
MedImg(34), No. 12, December 2015, pp. 2518-2534.
IEEE DOI 1601
biomedical optical imaging BibRef

Li, Q., Feng, B., Xie, L., Liang, P., Zhang, H., Wang, T.,
A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images,
MedImg(35), No. 1, January 2016, pp. 109-118.
IEEE DOI 1601
Accuracy BibRef

De, J., Cheng, L., Zhang, X., Lin, F., Li, H., Ong, K.H., Yu, W., Yu, Y., Ahmed, S.,
A Graph-Theoretical Approach for Tracing Filamentary Structures in Neuronal and Retinal Images,
MedImg(35), No. 1, January 2016, pp. 257-272.
IEEE DOI 1601
Biomedical imaging BibRef

Krause, M.[Michael], Alles, R.M.[Ralph Maria], Burgeth, B.[Bernhard], Weickert, J.[Joachim],
Fast retinal vessel analysis,
RealTimeIP(11), No. 2, February 2016, pp. 413-422.
Springer DOI 1602
BibRef

Gottemukkula, V., Saripalle, S., Tankasala, S.P., Derakhshani, R.,
Method for using visible ocular vasculature for mobile biometrics,
IET-Bio(5), No. 1, 2016, pp. 3-12.
DOI Link 1603
Gabor filters BibRef

Lermé, N.[Nicolas], Rossant, F.[Florence], Bloch, I.[Isabelle], Paques, M.[Michel], Koch, E.[Edouard], Benesty, J.[Jonathan],
A fully automatic method for segmenting retinal artery walls in adaptive optics images,
PRL(72), No. 1, 2016, pp. 72-81.
Elsevier DOI 1604
Active contour model BibRef

van Grinsven, M.J.J.P., van Ginneken, B., Hoyng, C.B., Theelen, T., Sánchez, C.I.,
Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images,
MedImg(35), No. 5, May 2016, pp. 1273-1284.
IEEE DOI 1605
Biomedical imaging BibRef

Favali, M.[Marta], Abbasi-Sureshjani, S.[Samaneh], ter Haar Romeny, B.M.[Bart M.], Sarti, A.[Alessandro],
Analysis of Vessel Connectivities in Retinal Images by Cortically Inspired Spectral Clustering,
JMIV(56), No. 1, September 2016, pp. 158-172.
Springer DOI 1605
BibRef

Abbasi-Sureshjani, S.[Samaneh], Favali, M.[Marta], Citti, G., Sarti, A.[Alessandro], ter Haar Romeny, B.M.[Bart M.],
Curvature Integration in a 5D Kernel for Extracting Vessel Connections in Retinal Images,
IP(27), No. 2, February 2018, pp. 606-621.
IEEE DOI 1712
Biomedical imaging, Blood vessels, Brain modeling, Junctions, Mathematical model, Retina, Visualization, spectral clustering BibRef

Duits, R.[Remco], Janssen, M.H.J.[Michiel H. J.], Hannink, J., Sanguinetti, G.R.[Gonzalo R.],
Locally Adaptive Frames in the Roto-Translation Group and Their Applications in Medical Imaging,
JMIV(56), No. 3, November 2016, pp. 367-402.
Springer DOI 1609
BibRef

Sanguinetti, G.R.[Gonzalo R.], Bekkers, E.[Erik], Duits, R.[Remco], Janssen, M.H.J.[Michiel H. J.], Mashtakov, A.[Alexey], Mirebeau, J.M.[Jean-Marie],
Sub-Riemannian Fast Marching in SE(2),
CIARP15(366-374).
Springer DOI 1511
Apply to vessel tree extraction. BibRef

Frucci, M.[Maria], Riccio, D.[Daniel], Sanniti di Baja, G.[Gabriella], Serino, L.[Luca],
Severe: Segmenting vessels in retina images,
PRL(82, Part 2), No. 1, 2016, pp. 162-169.
Elsevier DOI 1609
Retina scan images BibRef

Liskowski, P., Krawiec, K.,
Segmenting Retinal Blood Vessels With Deep Neural Networks,
MedImg(35), No. 11, November 2016, pp. 2369-2380.
IEEE DOI 1609
Biomedical imaging BibRef

Strisciuglio, N.[Nicola], Azzopardi, G.[George], Vento, M.[Mario], Petkov, N.[Nicolai],
Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters,
MVA(27), No. 8, November 2016, pp. 1137-1149.
Springer DOI 1612
BibRef
Earlier:
Multiscale Blood Vessel Delineation Using B-COSFIRE Filters,
CAIP15(II:300-312).
Springer DOI 1511
BibRef

Dash, J.[Jyotiprava], Bhoi, N.[Nilamani],
Detection of retinal blood vessels from ophthalmoscope images using morphological approach,
ELCVIA(16), No. 1, 2017, pp. 1-14.
DOI Link 1703
BibRef

Alkassar, S.[Sinan], Woo, W.L.[Wai-Lok], Dlay, S.S.[Satnam S.], Chambers, J.A.[Jonathon A.],
Robust Sclera Recognition System With Novel Sclera Segmentation and Validation Techniques,
SMCS(47), No. 3, March 2017, pp. 474-486.
IEEE DOI 1703
Active contours BibRef

Alkassar, S.[Sinan], Woo, W.L.[Wai-Lok], Dlay, S.S.[Satnam S.], Chambers, J.A.[Jonathon A.],
Sclera recognition: on the quality measure and segmentation of degraded images captured under relaxed imaging conditions,
IET-Bio(6), No. 4, July 2017, pp. 266-275.
DOI Link 1707
BibRef

Guo, J., Zhu, W., Shi, F., Xiang, D., Chen, H., Chen, X.,
A Framework for Classification and Segmentation of Branch Retinal Artery Occlusion in SD-OCT,
IP(26), No. 7, July 2017, pp. 3518-3527.
IEEE DOI 1706
Arteries, Image edge detection, Image segmentation, Retina, Shape, Visualization, AdaBoost classifier, Bayesian posterior probability, Branch retinal artery occlusion, graph search-graph cut, segmentation BibRef

Zhang, J.[Jiong], Chen, Y.[Yuan], Bekkers, E.[Erik], Wang, M.[Meili], Dashtbozorg, B.[Behdad], ter Haar Romeny, B.M.[Bart M.],
Retinal vessel delineation using a brain-inspired wavelet transform and random forest,
PR(69), No. 1, 2017, pp. 107-123.
Elsevier DOI 1706
Random forest BibRef

Morales, S., Naranjo, V., Angulo, J., Legaz-Aparicio, A.G., Verdú-Monedero, R.,
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Biomedical imaging, Blood vessels, Cameras, Feature extraction, Image resolution, Image segmentation, Retina, Retina, ultra-widefield BibRef

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blood vessels, eye, image colour analysis, image segmentation, learning (artificial intelligence), medical image processing, retinal image analysis BibRef

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Yan, Z., Yang, X., Cheng, K.T.,
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Image segmentation, Manuals, Observers, Retinal vessels, Skeleton, Thickness measurement, Retinal vessel segmentation, skeletal similarity BibRef

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Elsevier DOI 1905
Audio analysis, Event detection, Peaks of energy, Representation learning, Trainable feature extractors BibRef

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PR(87), 2019, pp. 157-169.
Elsevier DOI 1812
Particle filtering, Deep neural network, Deep Belief Net, Fundus image, Width estimation, Tracking BibRef

Sazak, Ç.[Çigdem], Nelson, C.J.[Carl J.], Obara, B.[Boguslaw],
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Elsevier DOI 1904
Image enhancement, Mathematical morphology, Bowler-hat transform, Blood vessel enhancement BibRef

Wang, X.H.[Xiao-Hong], Jiang, X.D.[Xu-Dong], Ren, J.F.[Jian-Feng],
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Elsevier DOI 1901
Fundus image, Retinal vessel segmentation, Cascade classification, Dimensionality reduction BibRef

Fan, Z., Lu, J., Wei, C., Huang, H., Cai, X., Chen, X.,
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IP(28), No. 5, May 2019, pp. 2367-2377.
IEEE DOI 1903
blood vessels, eye, feature extraction, image segmentation, medical image processing, hierarchical image matting model, vessel BibRef

Khowaja, S.A.[Sunder Ali], Khuwaja, P.[Parus], Ismaili, I.A.[Imdad Ali],
A framework for retinal vessel segmentation from fundus images using hybrid feature set and hierarchical classification,
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Automated Method for Retinal Artery/Vein Separation via Graph Search Metaheuristic Approach,
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IEEE DOI 1905
biomedical optical imaging, blood vessels, diseases, eye, feature extraction, graph theory, image classification, vessel keypoints BibRef

Han, M.[Myounghee], Kim, Y.[Yongjoo], Park, J.R.[Jang Ryul], Vakoc, B.J.[Benjamin J.], Oh, W.Y.[Wang-Yuhl], Ryu, S.[Sukyoung],
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IEEE DOI 1909
Image segmentation, Training, Retinal vessels, Blood vessels, Biomedical imaging, Image reconstruction, regularization BibRef

Motta, D., Casaca, W., Paiva, A.,
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IEEE DOI 1909
Retina, Biomedical imaging, Image registration, Feature extraction, Optimization, Blood vessels, Retinal image registration, optimal transport BibRef

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Springer DOI 1911
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Springer DOI 1912
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Retinal Vascular Network Topology Reconstruction and Artery/Vein Classification via Dominant Set Clustering,
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IEEE DOI 2002
Retinal images, dominant set clustering, blood vessel, vascular topology, Artery/vein classification BibRef

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DeepBranch: Deep Neural Networks for Branch Point Detection in Biomedical Images,
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IEEE DOI 2004
Biomedical imaging, Image reconstruction, Neurons, Image segmentation, Convolution, convolutional neural networks cascade BibRef

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Pipelines, Deep learning, Retinal vessels, Angiography, Training, Annotations, Fluorescein angiography, deep learning BibRef

Zhao, H.L.[Han-Li], Qiu, X.Q.[Xia-Qing], Lu, W.L.[Wang-Long], Huang, H.[Hui], Jin, X.G.[Xiao-Gang],
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dilated convolution, generative adversarial network, receptive field, retinal vessel segmentation BibRef

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IEEE DOI 2009
Task analysis, Visualization, Image segmentation, Feature extraction, Convolution, Retina, Machine learning, multi-task learning BibRef

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IEEE DOI 2011
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IEEE DOI 2301
Image segmentation, Retinal vessels, Biomedical imaging, optical coherence tomography angiography BibRef

Dash, S.[Sonali], Senapati, M.R.[Manas Ranjan], Sahu, P.K.[Pradip Kumar], Chowdary, P.S.R.,
Illumination normalized based technique for retinal blood vessel segmentation,
IJIST(31), No. 1, 2021, pp. 351-363.
DOI Link 2102
CLAHE, homomorphic normalization, hysteresis thresholding, retinal vasculature BibRef

Iqbal, M.[Mehwish], Riaz, M.M.[Muhammad Mohsin], Ghafoor, A.[Abdul], Ahmad, A.[Attiq],
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B-COSFIRE filter, detail enhancement, vessel extraction BibRef

Ma, Y., Hao, H., Xie, J., Fu, H., Zhang, J., Yang, J., Wang, Z., Liu, J., Zheng, Y., Zhao, Y.,
ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model,
MedImg(40), No. 3, March 2021, pp. 928-939.
IEEE DOI 2103
Measurement, Image segmentation, Statistical analysis, Optical coherence tomography, Ultraviolet sources, Retina, benchmark BibRef

Kipli, K.[Kuryati], Hoque, M.E.[Mohammed Enamul], Lim, L.T.[Lik Thai], Zulcaffle, T.M.A.[Tengku Mohd Afendi], Sahari, S.K.[Siti Kudnie], Mahmood, M.H.[Muhammad Hamdi],
Retinal image blood vessel extraction and quantification with Euclidean distance transform approach,
IET-IPR(14), No. 15, 15 December 2020, pp. 3718-3724.
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Chen, Z.Y.[Zhi-Yuan], Jin, W.[Wei], Zeng, X.B.[Xing-Bin], Xu, L.[Liang],
Retinal vessel segmentation based on task-driven generative adversarial network,
IET-IPR(14), No. 17, 24 December 2020, pp. 4599-4605.
DOI Link 2104
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Zhu, D.C.[Dong-Chen], Li, J.M.[Jia-Mao], Li, H.[Hang], Peng, J.Q.[Jing-Quan], Wang, X.S.[Xian-Shun], Zhang, X.L.[Xiao-Lin],
A Less-constrained Sclera Recognition Method based on Stem-and-leaf Branches Network,
PRL(145), 2021, pp. 43-49.
Elsevier DOI 2104
Biometrics, Sclera recognition, Vessels segmentation, Recognition neural network, Feature aggregation BibRef

Sun, M.[Muyi], Li, K.Q.[Kai-Qi], Qi, X.Q.[Xing-Qun], Dang, H.[Hao], Zhang, G.H.[Guan-Hong],
Contextual information enhanced convolutional neural networks for retinal vessel segmentation in color fundus images,
JVCIR(77), 2021, pp. 103134.
Elsevier DOI 2106
Retinal vessel segmentation, Color fundus image analysis, Semantic segmentation, Cascaded dilated module, Context fusion BibRef

Sayed, M.A.[Md. Abu], Saha, S.[Sajib], Rahaman, G.M.A.[G. M. Atiqur], Ghosh, T.K.[Tanmai K.], Kanagasingam, Y.[Yogesan],
An innovate approach for retinal blood vessel segmentation using mixture of supervised and unsupervised methods,
IET-IPR(15), No. 1, 2021, pp. 180-190.
DOI Link 2106
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Earlier: A4, A2, A3, A1, A5:
Retinal Blood Vessel Segmentation: A Semi-supervised Approach,
IbPRIA19(II:98-107).
Springer DOI 1910
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Liu, Y.P.[Yi-Peng], Rui, X.[Xue], Li, Z.Q.[Zhan-Qing], Zeng, D.X.[Dong-Xu], Li, J.[Jing], Chen, P.[Peng], Liang, R.H.[Rong-Hua],
Feature pyramid U-Net for retinal vessel segmentation,
IET-IPR(15), No. 8, 2021, pp. 1733-1744.
DOI Link 2106
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Tavakoli, M.[Meysam], Mehdizadeh, A.[Alireza], Shahri, R.P.[Reza Pourreza], Dehmeshki, J.[Jamshid],
Unsupervised automated retinal vessel segmentation based on Radon line detector and morphological reconstruction,
IET-IPR(15), No. 7, 2021, pp. 1484-1498.
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Zou, B.[Beiji], Fu, H.[Hongpu], Chen, Z.L.[Zai-Liang], Liu, Q.[Qing],
Ground truth free retinal vessel segmentation by learning from simple pixels,
IET-IPR(15), No. 6, 2021, pp. 1210-1220.
DOI Link 2106
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Peng, Y.Y.[Yuan-Yuan], Zhu, W.F.[Wei-Fang], Chen, Z.Y.[Zhong-Yue], Wang, M.[Meng], Geng, L.[Le], Yu, K.[Kai], Zhou, Y.[Yi], Wang, T.[Ting], Xiang, D.M.[Dao-Man], Chen, F.[Feng], Chen, X.J.[Xin-Jian],
Automatic Staging for Retinopathy of Prematurity With Deep Feature Fusion and Ordinal Classification Strategy,
MedImg(40), No. 7, July 2021, pp. 1750-1762.
IEEE DOI 2107
Feature extraction, Convolution, Blindness, Standards, Retinopathy, Retinal vessels, Data mining, Retinopathy of prematurity, fundus images BibRef

Das, S.[Sumanta], de Ghosh, I.[Ishita], Chattopadhyay, A.[Abir],
An efficient deep sclera recognition framework with novel sclera segmentation, vessel extraction and gaze detection,
SP:IC(97), 2021, pp. 116349.
Elsevier DOI 2107
DeepR, Gaze detection, Sclera biometric, Sclera recognition, Sclera segmentation, Vasculature segmentation BibRef

Hakim, L.[Lukman], Kavitha, M.S.[Muthu Subash], Yudistira, N.[Novanto], Kurita, T.[Takio],
Regularizer based on Euler characteristic for retinal blood vessel segmentation,
PRL(149), 2021, pp. 83-90.
Elsevier DOI 2108
Fundus image, Segmentation, Euler characteristic, Regularizer BibRef

Liu, Y.P.[Yi-Peng], Lv, Y.J.[Ya-Jun], Li, Z.Q.[Zhan-Qing], Li, J.[Jing], Liu, Y.[Yan], Chen, P.[Peng], Liang, R.H.[Rong-Hua],
Blood vessel and background separation for retinal image quality assessment,
IET-IPR(15), No. 11, 2021, pp. 2559-2571.
DOI Link 2108
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Sathananthavathi, V., Indumathi, G., Mahiya, R.[Rita], Priyadarshini, S.,
Improvement of thin retinal vessel extraction using mean matting method,
IJIST(31), No. 3, 2021, pp. 1455-1467.
DOI Link 2108
correlation, enhancement, extraction, features, fundus, trimap, wavelet transform BibRef

Zhao, R.[Ruohan], Li, Q.[Qin], Wu, J.R.[Jian-Rong], You, J.[Jane],
A nested U-shape network with multi-scale upsample attention for robust retinal vascular segmentation,
PR(120), 2021, pp. 107998.
Elsevier DOI 2109
Vascular segmentation, Retinal imaging, Dense U-Net, Multi-scale attention, Deep learning BibRef

Ding, L.[Li], Kuriyan, A.E.[Ajay E.], Ramchandran, R.S.[Rajeev S.], Wykoff, C.C.[Charles C.], Sharma, G.[Gaurav],
Weakly-Supervised Vessel Detection in Ultra-Widefield Fundus Photography via Iterative Multi-Modal Registration and Learning,
MedImg(40), No. 10, October 2021, pp. 2748-2758.
IEEE DOI 2110
Noise measurement, Training, Photography, Training data, Retinal vessels, Retinal vessel detection, noisy labels BibRef

Yang, X.[Xin], Li, Z.Q.[Zhi-Qiang], Guo, Y.Q.[Ying-Qing], Zhou, D.[Dake],
Retinal vessel segmentation based on an improved deep forest,
IJIST(31), No. 4, 2021, pp. 1792-1802.
DOI Link 2112
cascade forest, CNN, deep forest, model fusion, retinal blood vessel segmentation BibRef

Khan, T.M.[Tariq M.], Robles-Kelly, A.[Antonio], Naqvi, S.S.[Syed S.],
RC-Net: A Convolutional Neural Network for Retinal Vessel Segmentation,
DICTA21(01-07)
IEEE DOI 2201
Image segmentation, Image color analysis, Digital images, Benchmark testing, Information filters, Retinal vessels, Residual Connections BibRef

Wei, J.H.[Jia-Hong], Zhu, G.J.[Gui-Jie], Fan, Z.[Zhun], Liu, J.C.[Jin-Chao], Rong, Y.B.[Yi-Biao], Mo, J.J.[Jia-Jie], Li, W.J.[Wen-Ji], Chen, X.J.[Xin-Jian],
Genetic U-Net: Automatically Designed Deep Networks for Retinal Vessel Segmentation Using a Genetic Algorithm,
MedImg(41), No. 2, February 2022, pp. 292-307.
IEEE DOI 2202
Retinal vessels, Image segmentation, Computer architecture, Genetic algorithms, Network architecture, Biomedical imaging, neural architecture search (NAS) BibRef

Pal, M.N.[Mahua Nandy], Banerjee, M.[Minakshi],
Retinal vessel segmentation using a strip wise classification approach with grid search-based parameter selection,
IJCVR(12), No. 2, 2022, pp. 194-218.
DOI Link 2203
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Fu, Y.H.[Ying-Hua], Zhang, G.[Ge], Li, J.[Jiang], Pan, D.Y.[Dong-Yan], Wang, Y.X.[Yong-Xiong], Zhang, D.W.[Da-Wei],
Fovea localization by blood vessel vector in abnormal fundus images,
PR(129), 2022, pp. 108711.
Elsevier DOI 2206
Blood vessel vector (BVV), Fovea localization, Retinal raphe, Probability bubble, Region search BibRef

Mishra, S.[Suraj], Zhang, Y.Z.[Yi-Zhe], Chen, D.Z.[Danny Z.], Hu, X.S.[X. Sharon],
Data-Driven Deep Supervision for Medical Image Segmentation,
MedImg(41), No. 6, June 2022, pp. 1560-1574.
IEEE DOI 2206
Image segmentation, Feature extraction, Convolutional neural networks, Retinal vessels, Optical imaging, 2D and 3D images BibRef

Xu, P.F.[Peng-Fei], Zhao, G.[Gangjing], Liu, J.P.[Jin-Ping], Jahanshahi, H.[Hadi], Tang, Z.H.[Zhao-Hui], Gong, S.[Subo],
MCG&BA-Net: Retinal vessel segmentation using multiscale context gating and breakpoint attention,
IET-IPR(16), No. 11, 2022, pp. 3039-3056.
DOI Link 2208
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Li, Y.[Yang], Zhang, Y.[Yue], Cui, W.G.[Wei-Gang], Lei, B.[Baiying], Kuang, X.[Xihe], Zhang, T.[Teng],
Dual Encoder-Based Dynamic-Channel Graph Convolutional Network With Edge Enhancement for Retinal Vessel Segmentation,
MedImg(41), No. 8, August 2022, pp. 1975-1989.
IEEE DOI 2208
Image edge detection, Biomedical imaging, Image segmentation, Feature extraction, Deep learning, Convolution, Decoding, deep learning BibRef

Tan, Y.[Yubo], Yang, K.F.[Kai-Fu], Zhao, S.X.[Shi-Xuan], Li, Y.J.[Yong-Jie],
Retinal Vessel Segmentation With Skeletal Prior and Contrastive Loss,
MedImg(41), No. 9, September 2022, pp. 2238-2251.
IEEE DOI 2209
Image segmentation, Retinal vessels, Lesions, Deep learning, Skeleton, Feature extraction, Optical imaging, Fundus image, contrastive loss BibRef

Yang, X.[Xin], Liu, L.[Li], Li, T.[Tao],
MR-UNet: An UNet model using multi-scale and residual convolutions for retinal vessel segmentation,
IJIST(32), No. 5, 2022, pp. 1588-1603.
DOI Link 2209
deep learning, improved UNet, multi-scale features, retinal vessel segmentation BibRef

Mahapatra, S.[Sakambhari], Jena, U.R.[Uma Ranjan], Dash, S.[Sonali], Agrawal, S.,
A modified Coye algorithm for retinal vessel segmentation,
IJCVR(13), No. 1, 2023, pp. 73-90.
DOI Link 2212
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Hao, J.[Jinkui], Shen, T.[Ting], Zhu, X.[Xueli], Liu, Y.H.[Yong-Huai], Behera, A.[Ardhendu], Zhang, D.[Dan], Chen, B.[Bang], Liu, J.[Jiang], Zhang, J.[Jiong], Zhao, Y.T.[Yi-Tian],
Retinal Structure Detection in OCTA Image via Voting-Based Multitask Learning,
MedImg(41), No. 12, December 2022, pp. 3969-3980.
IEEE DOI 2212
Task analysis, Image segmentation, Feature extraction, Retinal vessels, Multitasking, Diseases, Bifurcation, OCTA, classification BibRef

Lyu, J.[Junyan], Zhang, Y.Q.[Yi-Qi], Huang, Y.J.[Yi-Jin], Lin, L.[Li], Cheng, P.[Pujin], Tang, X.Y.[Xiao-Ying],
AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation,
MedImg(41), No. 12, December 2022, pp. 3699-3711.
IEEE DOI 2212
Image segmentation, Task analysis, Training, Medical diagnostic imaging, Testing, Retinal vessels, Lesions, reinforcement learning BibRef

Liu, Y.H.[Yi-Hao], Carass, A.[Aaron], Zuo, L.[Lianrui], He, Y.F.[Yu-Fan], Han, S.[Shuo], Gregori, L.[Lorenzo], Murray, S.[Sean], Mishra, R.[Rohit], Lei, J.Q.[Jian-Qin], Calabresi, P.A.[Peter A.], Saidha, S.[Shiv], Prince, J.L.[Jerry L.],
Disentangled Representation Learning for OCTA Vessel Segmentation With Limited Training Data,
MedImg(41), No. 12, December 2022, pp. 3686-3698.
IEEE DOI 2212
Image segmentation, Manuals, Training, Decoding, Faces, Image reconstruction, Angiography, OCT, OCTA, vessel segmentation BibRef

Erwin, Putra, H.K.[Hadrians Kesuma], Suprihatin, B.[Bambang], Fathoni,
Retinal Blood Vessel Extraction Using a New Enhancement Technique of Modified Convolution Filters and Sauvola Thresholding,
IJIG(23), No. 1 2023, pp. 2350006.
DOI Link 2302
BibRef

Challoob, M.[Mohsin], Gao, Y.S.[Yong-Sheng], Busch, A.[Andrew], Nikzad, M.[Mohammad],
Separable Paravector Orientation Tensors for Enhancing Retinal Vessels,
MedImg(42), No. 3, March 2023, pp. 880-893.
IEEE DOI 2303
Tensors, Retinal vessels, Kernel, Lighting, Bandwidth, Eigenvalues and eigenfunctions, Frequency-domain analysis, tensor analysis BibRef

Fhima, J.[Jonathan], van Eijgen, J.[Jan], Stalmans, I.[Ingeborg], Men, Y.[Yevgeniy], Freiman, M.[Moti], Behar, J.A.[Joachim A.],
Pvbm: A Python Vasculature Biomarker Toolbox Based on Retinal Blood Vessel Segmentation,
MCV22(296-312).
Springer DOI 2304
BibRef

Harish, B.S., Maheshan, M.S., Roopa, C.K., Rangan, R.K.[R. Kasturi],
An improved sclera recognition using kernel entropy component analysis method,
IJCVR(13), No. 3, 2023, pp. 304-315.
DOI Link 2305
BibRef

Liu, Y.P.[Yi-Peng], Zeng, D.X.[Dong-Xu], Li, Z.Q.[Zhan-Qing], Chen, P.[Peng], Liang, R.H.[Rong-Hua],
SS-Norm: Spectral-spatial normalization for single-domain generalization with application to retinal vessel segmentation,
IET-IPR(17), No. 7, 2023, pp. 2168-2181.
DOI Link 2305
convolutional neural nets, image segmentation, medical image processing BibRef

van den Berg, N.J.[Nicky J.], Zhang, S.[Shuhe], Smets, B.M.N.[Bart M. N.], Berendschot, T.T.J.M.[Tos T. J. M.], Duits, R.[Remco],
Geodesic Tracking of Retinal Vascular Trees with Optical and TV-Flow Enhancement in Se(2),
SSVM23(525-537).
Springer DOI 2307
BibRef

Li, Y.[Yang], Zhang, Y.[Yue], Liu, J.Y.[Jing-Yu], Wang, K.[Kang], Zhang, K.[Kai], Zhang, G.S.[Gen-Sheng], Liao, X.F.[Xiao-Feng], Yang, G.[Guang],
Global Transformer and Dual Local Attention Network via Deep-Shallow Hierarchical Feature Fusion for Retinal Vessel Segmentation,
Cyber(53), No. 9, September 2023, pp. 5826-5839.
IEEE DOI 2309
BibRef

Gao, W.W.[Wei-Wei], Fan, B.[Bo], Fang, Y.[Yu], Shan, M.[Mingtao], Song, N.[Nan],
Detection and location of microaneurysms in fundus images based on improved YOLOv4 with IFCM,
IET-IPR(17), No. 11, 2023, pp. 3349-3357.
DOI Link 2310
fundus image, improved fuzzy C-means (IFCM), microaneurysm, SENet, YOLOv4 BibRef

Chen, J.[Jian], Wan, J.[Jiaze], Fang, Z.[Zhenghan], Wei, L.F.[Li-Fang],
LMSA-Net: A lightweight multi-scale aware network for retinal vessel segmentation,
IJIST(33), No. 5, 2023, pp. 1515-1530.
DOI Link 2310
lightweight network, multi-scale awareness, retinal vessel segmentation, semantic information BibRef

Wang, X.C.[Xiao-Chen], Ding, Y.H.[Yan-Hui], Zheng, Y.J.[Yuan-Jie],
Multiscale attention network for retinal vein occlusion classification with multicolor image,
IJIST(33), No. 6, 2023, pp. 2012-2022.
DOI Link 2311
convolutional neural networks, multicolor images, multiscale attention, retinal image classification, retinal vein occlusion BibRef

Das, S.[Sumanta], Ghosh, I.D.[Ishita De], Chattopadhyay, A.[Abir],
An investigation into automated age estimation using sclera images: a novel modality,
IJCVR(14), No. 1, 2024, pp. 42-62.
DOI Link 2312
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Huang, L.Y.[Lin-Yuan], Liu, F.[Feng],
Width measurement of retinal vessels using cubic spline fitting and extended edge-searching mode,
IJIST(34), No. 2, 2024, pp. e22996.
DOI Link 2402
central light reflex, cubic spline fitting, extended edge-searching mode, retinal vessel, width measurement BibRef

Jalali, Y.[Yeganeh], Fateh, M.[Mansoor], Rezvani, M.[Mohsen],
VGA-Net: Vessel graph based attentional U-Net for retinal vessel segmentation,
IET-IPR(18), No. 8, 2024, pp. 2191-2213.
DOI Link Code:
WWW Link. 2406
biomedical imaging, image processing, image segmentation, medical image processing BibRef

Bal, A.B.[Aditi Basu], Guo, X.Y.[Xiao-Yang], Needham, T.[Tom], Srivastava, A.[Anuj],
Statistical Analysis of Complex Shape Graphs,
PAMI(46), No. 12, December 2024, pp. 8788-8805.
IEEE DOI 2411
Shape, Blood vessels, Biomedical imaging, Databases, Analytical models, Topology, Mathematical models, shape models BibRef

Cao, Y.X.[Yun-Xiang], Chen, L.[Li], Wang, Y.[Yubo], Feng, Z.[Zhida], Liu, X.M.[Xiao-Ming],
Unleashing Fine-Coarse Curve Perception Via Trunk-Branch Perturbation,
ICIP24(1113-1119)
IEEE DOI 2411
Measurement, Image segmentation, Fuses, Perturbation methods, Blood vessels, Retina, Robustness, Curve structure, Perturbation BibRef

Zhang, W.Y.[Wei-Yi], Shi, D.[Danli], He, M.G.[Ming-Guang],
Improving Consistency in Cardiovascular Disease Risk Assessment: Cross-Camera Adaptation for Retinal Images,
DEF-AI-MIA24(5194-5199)
IEEE DOI 2410
Adaptation models, Computational modeling, Medical services, Retina, Cameras, Image-to-image Translation, Cardiovascular Disease BibRef

Mehmood, M.[Mehwish], Alsharari, M.[Majed], Iqbal, S.[Shahzaib], Spence, I.[Ivor], Fahim, M.[Muhammad],
RetinaLiteNet: A Lightweight Transformer based CNN for Retinal Feature Segmentation,
WiCV24(2454-2463)
IEEE DOI 2410
Deep learning, Image segmentation, Biomedical optical imaging, Image analysis, Drives, Blood vessels, Optical imaging, Retinal blood vessel segmentation BibRef

Dulau, I.[Idris], Helmer, C.[Catherine], Delcourt, C.[Cecile], Beurton-Aimar, M.[Marie],
Ensuring a connected structure for Retinal Vessels Deep-Learning Segmentation,
CVAMD23(2356-2365)
IEEE DOI 2401
BibRef

Bhati, A.[Amit], Jain, S.[Samir], Gour, N.[Neha], Khanna, P.[Pritee], Ojha, A.[Aparajita], Werghi, N.[Naoufel],
A Shallow U-Net with Split-Fused Attention Mechanism for Retinal Vessel Segmentation,
ICIP23(3205-3209)
IEEE DOI 2312
BibRef

Mohan, S.[Shikhar], Bhattacharya, S.[Saumik], Ghosh, S.[Sayantari],
Attention W-Net: Improved Skip Connections for Better Representations,
ICPR22(217-222)
IEEE DOI 2212
Training, Image segmentation, Neural networks, Performance gain, Distortion, Retinal vessels, Decoding BibRef

Cai, B.[Binke], Ma, L.Y.[Li-Yan],
A Transformer-based Cascade Network with Boundary Enhancement Loss for Retinal Vessel Segmentation,
ICPR22(4292-4298)
IEEE DOI 2212
Image segmentation, Sensitivity, Image databases, Neural networks, Medical services, Manuals, Transformers BibRef

Wang, J.[Jie], Zhong, C.L.[Chao-Liang], Feng, C.[Cheng], Sun, J.[Jun], Yokota, Y.[Yasuto],
Feature Disentanglement for Cross-Domain Retina Vessel Segmentation,
ICIP21(26-30)
IEEE DOI 2201
Degradation, Image segmentation, Neural networks, Retina, Feature extraction, Task analysis, Feature disentanglement, Unsupervised domain adaptation BibRef

Li, C.X.[Chen-Xin], Zhang, Y.L.[Yun-Long], Liang, Z.H.[Zhe-Han], Ma, W.[Wenao], Huang, Y.[Yue], Ding, X.H.[Xing-Hao],
Consistent Posterior Distributions Under Vessel-Mixing: A Regularization for Cross-Domain Retinal Artery/Vein Classification,
ICIP21(61-65)
IEEE DOI 2201
Deep learning, Image segmentation, Protocols, Perturbation methods, Supervised learning, Imaging, Cross-domain learning, unsupervised domain adaptation BibRef

Wang, J.[Jun], Zhao, Y.[Yang], Qian, L.L.[Ling-Long], Yu, X.H.[Xiao-Han], Gao, Y.S.[Yong-Sheng],
EAR-NET: Error Attention Refining Network for Retinal Vessel Segmentation,
DICTA21(1-7)
IEEE DOI 2201
Training, Sensitivity, Retinopathy, Digital images, Refining, Focusing, Blood vessels BibRef

Saxena, S.[Suraj], Lal, K.[Kanhaiya], Joshi, S.[Sharad],
Retinal Vessel Segmentation Using Blending-Based Conditional Generative Adversarial Networks,
CAIP21(I:135-144).
Springer DOI 2112
BibRef

Zeng, H.[HongWei], Yi, X.[XingWen], Liang, S.[ShanShan],
Mix: A Potential Image Augmentation Method on Retinal Vessel Segmentation,
ICIVC21(140-143)
IEEE DOI 2112
Image segmentation, Costs, Magnetic resonance imaging, Computed tomography, Robustness, Retinal vessels, Image Augmentation BibRef

Carrillo-Gomez, C.[Cesar], Nakano, M.[Mariko], Gonzalez-H.Leon, A.[Ana], Romo-Aguas, J.C.[Juan Carlos], Quiroz-Mercado, H.[Hugo], Lopez-Garcia, O.[Osvaldo],
Neovascularization Detection on Optic Disc Region Using Deep Learning,
MCPR21(111-120).
Springer DOI 2108
BibRef

Reyes-Figueroa, A.[Alan], Rivera, M.[Mariano],
W-net: A Convolutional Neural Network for Retinal Vessel Segmentation,
MCPR21(355-368).
Springer DOI 2108
BibRef

Guo, C.[Changlu], Szemenyei, M.[Márton], Yi, Y.[Yugen], Wang, W.[Wenle], Chen, B.[Buer], Fan, C.Q.[Chang-Qi],
SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation,
ICPR21(1236-1242)
IEEE DOI 2105
Training, Image segmentation, Pediatrics, Image edge detection, Neural networks, Blood vessels, Retinal vessels, Segmentation, spatial attention BibRef

Zhang, Y.[Yi], Chen, Y.X.[Yi-Xuan], Zhang, K.[Kai],
PCANet: Pyramid Context-aware Network for Retinal Vessel Segmentation,
ICPR21(2073-2080)
IEEE DOI 2105
Hypertension, Image segmentation, Adaptive systems, Navigation, Arteriosclerosis, Retinal vessels, integrated test-time augmentation BibRef

Kamran, S.A.[Sharif Amit], Hossain, K.F.[Khondker Fariha], Tavakkoli, A.[Alireza], Zuckerbrod, S.L.[Stewart Lee],
Attention2AngioGAN: Synthesizing Fluorescein Angiography from Retinal Fundus Images using Generative Adversarial Networks,
ICPR21(9122-9129)
IEEE DOI 2105
Photography, Training, Angiography, Retina, Generators, Robustness, Reproducibility of results, Generative Adversarial Networks, Residual Attention BibRef

Kamran, S.A.[Sharif Amit], Hossain, K.F.[Khondker Fariha], Tavakkoli, A.[Alireza], Zuckerbrod, S.[Stewart], Baker, S.A.[Salah A.], Sanders, K.M.[Kenton M.],
Fundus2angio: A Conditional Gan Architecture for Generating Fluorescein Angiography Images from Retinal Fundus Photography,
ISVC20(II:125-138).
Springer DOI 2103
BibRef

Badeka, E., Papadopoulou, C.I., Papakostas, G.A.,
Evaluation of LBP Variants in Retinal Blood Vessels Segmentation Using Machine Learning,
ISCV20(1-7)
IEEE DOI 2011
biomedical optical imaging, blood vessels, decision trees, eye, feature extraction, image classification, image segmentation, computer vision BibRef

Kushol, R., Salekin, M.S.,
Rbvs-Net: A Robust Convolutional Neural Network For Retinal Blood Vessel Segmentation,
ICIP20(398-402)
IEEE DOI 2011
Biomedical imaging, Image segmentation, Blood vessels, Retinal vessels, Machine learning, Feature extraction, transfer learning BibRef

Lahiri, A., Jain, V., Mondal, A., Biswas, P.K.,
Retinal Vessel Segmentation Under Extreme Low Annotation: A Gan Based Semi-Supervised Approach,
ICIP20(418-422)
IEEE DOI 2011
Training, Generators, Image segmentation, Biomedical imaging, Generative adversarial networks, adversarial learning BibRef

Li, P., Deng, Q., Li, H.,
The Arteriovenous Classification in Retinal Images by U-net and Tracking Algorithm,
ICIVC20(182-187)
IEEE DOI 2009
Retina, Image segmentation, Feature extraction, Classification algorithms, Veins, Arteries, Biomedical imaging, vessel tracking BibRef

Li, L., Verma, M., Nakashima, Y., Nagahara, H., Kawasaki, R.,
IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks,
WACV20(3645-3654)
IEEE DOI 2006
Image segmentation, Retina, Training, Biomedical imaging, Standards, Task analysis, Machine learning BibRef

Zhao, M., Hamarneh, G.,
Retinal Image Classification via Vasculature-Guided Sequential Attention,
VRMI19(381-387)
IEEE DOI 2004
Retina, Retinopathy, Machine learning, Biomedical imaging, Blood vessels, Feature extraction, deep learning, attention, lstm, retinal image BibRef

Challoob, M.[Mohsin], Gao, Y.S.[Yong-Sheng],
A Local Flow Phase Stretch Transform for Robust Retinal Vessel Detection,
ACIVS20(251-261).
Springer DOI 2003
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Garifullin, A.[Azat], Lensu, L.[Lasse], Uusitalo, H.[Hannu],
On the Uncertainty of Retinal Artery-vein Classification with Dense Fully-convolutional Neural Networks,
ACIVS20(87-98).
Springer DOI 2003
BibRef

Thanh, D.N.H., Sergey, D., Surya Prasath, V.B., Hai, N.H.,
Blood Vessels Segmentation Method for Retinal Fundus Images Based On Adaptive Principal Curvature and Image Derivative Operators,
PTVSBB19(211-218).
DOI Link 1912
BibRef

Li, D., Dharmawan, D.A., Ng, B.P., Rahardja, S.,
Residual U-Net for Retinal Vessel Segmentation,
ICIP19(1425-1429)
IEEE DOI 1910
Deep learning, fundus images, residual blocks, vessel segmentation BibRef

Trimeche, I.[Iyed], Rossant, F.[Florence], Bloch, I.[Isabelle], Paques, M.[Michel],
Fully automatic CNN-based segmentation of retinal bifurcations in 2D adaptive optics ophthalmoscopy images,
IPTA20(1-6)
IEEE DOI 2206
BibRef
Earlier:
Segmentation of Retinal Arterial Bifurcations in 2D Adaptive Optics Ophthalmoscopy Images,
ICIP19(1490-1494)
IEEE DOI 1910
Image segmentation, Neural networks, Bifurcation, Adaptive optics, Retinal vessels, Biomedical imaging, Active contours, adaptive optics ophthalmoscopy images. segmentation, retinal arterial bifurcations BibRef

Cherukuri, V., Kumar B G, V., Bala, R., Monga, V.,
Multi-Scale Regularized Deep Network for Retinal Vessel Segmentation,
ICIP19(824-828)
IEEE DOI 1910
segmentation, deep learning, priors. BibRef

Zhang, J., An, C., Dai, J., Amador, M., Bartsch, D., Borooah, S., Freeman, W.R., Nguyen, T.Q.,
Joint Vessel Segmentation and Deformable Registration on Multi-Modal Retinal Images Based on Style Transfer,
ICIP19(839-843)
IEEE DOI 1910
Multi-Modal, Retinal Images, Deformable Registration, Vessel Segmentation, Style Transfer BibRef

Wargnier-Dauchelle, V.[Valentine], Simon-Chane, C.[Camille], Histace, A.[Aymeric],
Retinal Blood Vessels Segmentation: Improving State-of-the-Art Deep Methods,
CAIPWS19(5-16).
Springer DOI 1909
BibRef

Araújo, R.J.[Ricardo J.], Cardoso, J.S.[Jaime S.], Oliveira, H.P.[Hélder P.],
A Single-Resolution Fully Convolutional Network for Retinal Vessel Segmentation in Raw Fundus Images,
CIAP19(II:59-69).
Springer DOI 1909
BibRef

Sheng, B., Li, P., Mo, S., Li, H., Hou, X., Wu, Q., Qin, J., Fang, R., Feng, D.D.,
Retinal Vessel Segmentation Using Minimum Spanning Superpixel Tree Detector,
Cyber(49), No. 7, July 2019, pp. 2707-2719.
IEEE DOI 1905
Image segmentation, Retinal vessels, Detectors, Feature extraction, Image color analysis, Diabetes, Feature extraction, vessel segmentation BibRef

He, Q., Zou, B., Zhu, C., Liu, X., Fu, H., Wang, L.,
Multi-Label Classification Scheme Based on Local Regression for Retinal Vessel Segmentation,
ICIP18(2765-2769)
IEEE DOI 1809
Image segmentation, Retinal vessels, Blood vessels, Biomedical imaging, Training, Task analysis, neural network, multi-label classification BibRef

Hajabdollahi, M., Esfandiarpoor, R., Najarian, K., Karimi, N., Samavi, S., Reza-Soroushmeh, S.M.,
Low Complexity Convolutional Neural Network for Vessel Segmentation in Portable Retinal Diagnostic Devices,
ICIP18(2785-2789)
IEEE DOI 1809
Quantization (signal), Image segmentation, Complexity theory, Retinal vessels, Training, Convolutional neural networks, network binarization BibRef

Xu, D., Lim, G., Lee, M.L.[M. Li], Hsu, W.,
A Differential-Based Approach for Vessel Type Classification in Retinal Images,
ICIP18(2790-2794)
IEEE DOI 1809
Retina, Arteries, Veins, Optical imaging, Image color analysis, Photonics, Blood, retinal image, artery-vein classification BibRef

Ding, L., Kuriyan, A., Ramchandran, R., Sharma, G.,
Retinal Vessel Detection in Wide-Field Fluorescein Angiography with Deep Neural Networks: A Novel Training Data Generation Approach,
ICIP18(356-360)
IEEE DOI 1809
Training data, Neural networks, Angiography, Image color analysis, Generators, Retinal vessels, Fluorescein angiography, retinal image analysis BibRef

Krylov, A.[Andrey], Nasonov, A.[Andrey], Chesnakov, K.[Konstantin], Nasonova, A.[Alexandra], Jin, S.O.[Seung Oh], Kang, U.[Uk], Park, S.M.[Sang Min],
Vessel Preserving CNN-Based Image Resampling of Retinal Images,
ICIAR18(589-597).
Springer DOI 1807
BibRef

Meyer, M.I.[Maria Ines], Galdran, A.[Adrian], Costa, P.[Pedro], Mendonça, A.M.[Ana Maria], Campilho, A.[Aurélio],
Deep Convolutional Artery/Vein Classification of Retinal Vessels,
ICIAR18(622-630).
Springer DOI 1807
BibRef

AlBadawi, S.[Sufian], Fraz, M.M.,
Arterioles and Venules Classification in Retinal Images Using Fully Convolutional Deep Neural Network,
ICIAR18(659-668).
Springer DOI 1807
BibRef

Sehirli, E., Turan, M.K., Demiral, E.,
An Algorithm to Detect the Retinal Region of Interest,
GeoAdvances17(95-97).
DOI Link 1805
BibRef

Sabaz, F., Atila, U.,
ROI Detection and Vessel Segmentation in Retinal Image,
GeoAdvances17(85-89).
DOI Link 1805
BibRef

Welikala, R.A., Fraz, M.M., Habib, M.M., Daniel-Tong, S., Yates, M., Foster, P.J., Whincup, P.H., Rudnicka, A.R., Owen, C.G., Strachan, D.P., Barman, S.A.,
Automated quantification of retinal vessel morphometry in the UK biobank cohort,
IPTA17(1-6)
IEEE DOI 1804
biomedical optical imaging, blood vessels, data analysis, diseases, eye, image segmentation, medical image processing, UK Biobank Eye, UK Biobank BibRef

Soomro, T.A., Afifi, A.J., Gao, J., Hellwich, O., Khan, M.A.U., Paul, M., Zheng, L.,
Boosting Sensitivity of a Retinal Vessel Segmentation Algorithm with Convolutional Neural Network,
DICTA17(1-8)
IEEE DOI 1804
blood vessels, diseases, eye, image segmentation, medical image processing, neural nets, Retinal vessels BibRef

Feng, Z., Yang, J., Yao, L.,
Patch-based fully convolutional neural network with skip connections for retinal blood vessel segmentation,
ICIP17(1742-1746)
IEEE DOI 1803
Biomedical imaging, Blood vessels, Computer architecture, Entropy, Image segmentation, Retina, Training, Class-balancing Loss, Retinal Blood Vessel Segmentation BibRef

Riccio, D.[Daniel], Brancati, N.[Nadia], Frucci, M.[Maria], Gragnaniello, D.[Diego],
An Unsupervised Approach for Eye Sclera Segmentation,
CIARP17(550-557).
Springer DOI 1802
BibRef

Pinheiro, J.F.[Jullyana Fialho], de Almeida, J.D.S.[João Dallyson Sousa], Junior, G.B.[Geraldo Braz], de Paiva, A.C.[Anselmo Cardoso], Silva, A.C.[Aristófanes Corrêa],
Sclera Segmentation in Face Images Using Image Foresting Transform,
CIARP17(229-236).
Springer DOI 1802
BibRef

Brancati, N.[Nadia], Frucci, M.[Maria], Gragnaniello, D.[Diego], Riccio, D.[Daniel],
Retinal Vessels Segmentation Based on a Convolutional Neural Network,
CIARP17(119-126).
Springer DOI 1802
BibRef

Savelli, B.[Benedetta], Marchesi, A.[Agnese], Bria, A.[Alessandro], Marrocco, C.[Claudio], Molinara, M.[Mario], Tortorella, F.[Francesco],
Retinal Vessel Segmentation Through Denoising and Mathematical Morphology,
CIAP17(II:267-276).
Springer DOI 1711
BibRef

Lahiri, A., Ayush, K., Biswas, P.K., Mitra, P.,
Generative Adversarial Learning for Reducing Manual Annotation in Semantic Segmentation on Large Scale Miscroscopy Images: Automated Vessel Segmentation in Retinal Fundus Image as Test Case,
Microscopy17(794-800)
IEEE DOI 1709
Biomedical imaging, Generators, Image segmentation, Manuals, Semantics, Training BibRef

Challoob, M.[Mohsin], Gao, Y.S.[Yong-Sheng],
Retinal Vessel Segmentation Using Matched Filter with Joint Relative Entropy,
CAIP17(I: 228-239).
Springer DOI 1708
BibRef

Gou, D.D.[Duo-Duo], Ma, T.[Tong], Wei, Y.[Ying],
A novel retinal vessel extraction method based on dynamic scales allocation,
ICIVC17(145-149)
IEEE DOI 1708
Biomedical imaging, Blood vessels, Image segmentation, Matched filters, Resource management, Retinal vessels, dynamic scales allocation, image sub-blocking, multiscale matched filter, retinal, vessel, extraction BibRef