Journal Article2011Cited by 65

High speed detection of retinal blood vessels in fundus image using phase congruency

M Ashraful Amin, Hong Yan

Soft Computing

Springer-Verlag, Vol. 15, Issue 6, pp. 1217–1230, ISBN: 1432-7643

  • Good Health and Well-being — Relevance: 68%
DOI: 10.1007/s00500-010-0574-2URL

Keywords

  • Phase congruency
  • DRIVE dataset
  • retinal vessel detection
  • log-Gabor wavelets
  • fundus images
  • STARE dataset
  • threshold selection
  • retinal disease diagnosis
  • ROC curve
  • real-time segmentation

CCDS Authors

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

References

  1. 1.John Canny. (1986). A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6), 679–698[10.1109/tpami.1986.4767851]
  2. 2.Tom Fawcett. (2005). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874[10.1016/j.patrec.2005.10.010]
  3. 3.David Marr, Ellen C. Hildreth. (1980). Theory of edge detection. Proceedings of the Royal Society of London. Series B, Biological sciences, 207(1167), 187–217[10.1098/rspb.1980.0020]
  4. 4.Joes Staal, Michael D. Abràmoff, Meindert Niemeijer, Max A. Viergever, Bram van Ginneken. (2004). Ridge-Based Vessel Segmentation in Color Images of the Retina. IEEE Transactions on Medical Imaging, 23(4), 501–509[10.1109/tmi.2004.825627]
  5. 5.David Field. (1987). Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America A, 4(12), 2379[10.1364/josaa.4.002379]
  6. 6.A.D. Hoover, Valentina L. Kouznetsova, Michael H. Goldbaum. (2000). Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging, 19(3), 203–210[10.1109/42.845178]
  7. 7.Subhasis Chaudhuri, S. Chatterjee, Norman P. Katz, Mark Nelson, Michael H. Goldbaum. (1989). Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Transactions on Medical Imaging, 8(3), 263–269[10.1109/42.34715]
  8. 8.João V. B. Soares, J. J. G. Leandro, Roberto M. César, Herbert F. Jelinek, Michael J. Cree. (2006). Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Transactions on Medical Imaging, 25(9), 1214–1222[10.1109/tmi.2006.879967]
  9. 9.Weisstein, Eric W. (2003). CRC concise encyclopedia of mathematics. Choice Reviews Online, 40(11), 40–6165[10.5860/choice.40-6165]
  10. 10.Elisa Ricci, R. Perfetti. (2007). Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification. IEEE Transactions on Medical Imaging, 26(10), 1357–1365[10.1109/tmi.2007.898551]
  11. 11.Meindert Niemeijer, Joes Staal, Bram van Ginneken, Marco Loog, Michael D. Abràmoff. (2004). Comparative study of retinal vessel segmentation methods on a new publicly available database. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE, 5370, 648[10.1117/12.535349]
  12. 12.John A. Swets. (1996). Signal Detection Theory and ROC Analysis in Psychology and Diagnostics: Collected Papers. Medical Entomology and Zoology
  13. 13.Xiaoyi Jiang, Daniel S. Mojon. (2003). Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(1), 131–137[10.1109/tpami.2003.1159954]
  14. 14.Maria Concetta Morrone, Robyn Owens. (1987). Feature detection from local energy. Pattern Recognition Letters, 6(5), 303–313[10.1016/0167-8655(87)90013-4]
  15. 15.Maria Concetta Morrone, John Ross, David C. Burr, Robyn Owens. (1986). Mach bands are phase dependent. Nature, 324(6094), 250–253[10.1038/324250a0]
  16. 16.Benson S. Y. Lam, Hong Yan. (2008). A Novel Vessel Segmentation Algorithm for Pathological Retina Images Based on the Divergence of Vector Fields. IEEE Transactions on Medical Imaging, 27(2), 237–246[10.1109/tmi.2007.909827]
  17. 17.Di Wu, Miao Zhang, J.-C. Liu, Wendall Bauman. (2006). On the Adaptive Detection of Blood Vessels in Retinal Images. IEEE Transactions on Biomedical Engineering, 53(2), 341–343[10.1109/tbme.2005.862571]
  18. 18.Shinichi Tamura, Yasukazu Okamoto, Kenji Yanashima. (1988). Zero-crossing interval correction in tracing eye-fundus blood vessels. Pattern Recognition, 21(3), 227–233[10.1016/0031-3203(88)90057-x]
  19. 19.S. Venkatesh, Robyn Owens. (1989). An energy feature detection scheme. Deakin Research Online (Deakin University)
  20. 20.Koichiro Akita, Hideki Kuga. (1982). <title>Pattern Recognition Of Blood Vessel Networks In Ocular Fundus Images</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE, 0375, 436–443[10.1117/12.934679]
  21. 21.Li Qin, Jane You, Lei Zhang, Prabir Bhattacharya. (2006). A Multiscale Approach to Retinal Vessel Segmentation Using Gabor Filters and Scale Multiplication. , 3521–3527[10.1109/icsmc.2006.384665]
  22. 22.A. Grasselli. (1969). Automatic interpretation and classification of images.
  23. 23.Faraz Oloumi, Rangaraj M. Rangayyan, Foad Oloumi, Peyman Eshghzadeh-Zanjani, Fábio J. Ayres. (2007). Detection of Blood Vessels in Fundus Images of the Retina using Gabor Wavelets. Conference proceedings, 58, 6451–6454[10.1109/iembs.2007.4353836]
  24. 24.Hiroshi Nagahashi, M. Nakatsuyama. (1986). A Pattern Description and Generation Method of Structural Characters. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(1), 112–118[10.1109/tpami.1986.4767759]
  25. 25.Shogo Yamamoto, Hisatake Yokouchi. (1976). Automatic Recognition of Color Fundus Photographs. , 385–398[10.1007/978-1-4684-0769-3_24]

Cited By

  1. 1.M.M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A.R. Rudnicka, C.G. Owen, S.A. Barman. (2012). Blood vessel segmentation methodologies in retinal images – A survey. Computer Methods and Programs in Biomedicine, 108(1), 407–433[10.1016/j.cmpb.2012.03.009]
  2. 2.Jose Ignacio Orlando, Elena Prokofyeva, Matthew B. Blaschko. (2016). A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images. IEEE Transactions on Biomedical Engineering, 64(1), 16–27[10.1109/tbme.2016.2535311]
  3. 3.M.M. Fraz, S.A. Barman, P. Remagnino, A. Hoppe, A. Basit, B. Uyyanonvara, A.R. Rudnicka, C.G. Owen. (2011). An approach to localize the retinal blood vessels using bit planes and centerline detection. Computer Methods and Programs in Biomedicine, 108(2), 600–616[10.1016/j.cmpb.2011.08.009]
  4. 4.Jen Hong Tan, U. Rajendra Acharya, Sulatha V. Bhandary, Kuang Chua Chua, Sobha Sivaprasad. (2017). Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. Journal of Computational Science, 20, 70–79[10.1016/j.jocs.2017.02.006]
  5. 5.Nagendra Pratap Singh, Rajeev Srivastava. (2016). Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter. Computer Methods and Programs in Biomedicine, 129, 40–50[10.1016/j.cmpb.2016.03.001]
  6. 6.M. M Fraz, A. Basit, S. A. Barman. (2012). Application of Morphological Bit Planes in Retinal Blood Vessel Extraction. Journal of Imaging Informatics in Medicine, 26(2), 274–286[10.1007/s10278-012-9513-3]
  7. 7.Ankita Gupta, Rita Chhikara. (2018). Diabetic Retinopathy: Present and Past. Procedia Computer Science, 132, 1432–1440[10.1016/j.procs.2018.05.074]
  8. 8.Khan Bahadar Khan, Amir A. Khaliq, Abdul Jalil, Muhammad Aksam Iftikhar, Najeeb Ullah, Muhammad Waqar Aziz, Kifayat Ullah, Muhammad Shahid. (2018). A review of retinal blood vessels extraction techniques: challenges, taxonomy, and future trends. Pattern Analysis and Applications, 22(3), 767–802[10.1007/s10044-018-0754-8]
  9. 9.Birendra Biswal, Thotakura Pooja, N. Bala Subrahmanyam. (2017). Robust retinal blood vessel segmentation using line detectors with multiple masks. IET Image Processing, 12(3), 389–399[10.1049/iet-ipr.2017.0329]
  10. 10.Toufique Ahmed Soomro, Junbin Gao, Tariq Khan, Ahmad Fadzil M. Hani, Mohammad A. U. Khan, Manoranjan Paul. (2017). Computerised approaches for the detection of diabetic retinopathy using retinal fundus images: a survey. Pattern Analysis and Applications, 20(4), 927–961[10.1007/s10044-017-0630-y]
  11. 11.Aditya Gupta, Even Bringsdal, Kristian Muri Knausgård, Morten Goodwin. (2022). Accurate Wound and Lice Detection in Atlantic Salmon Fish Using a Convolutional Neural Network. Fishes, 7(6), 345[10.3390/fishes7060345]
  12. 12.Sushil Kumar Saroj, Rakesh Kumar, Nagendra Pratap Singh. (2020). Fréchet PDF based Matched Filter Approach for Retinal Blood Vessels Segmentation. Computer Methods and Programs in Biomedicine, 194, 105490[10.1016/j.cmpb.2020.105490]
  13. 13.Temitope Mapayi, Serestina Viriri, Jules-Raymond Tapamo. (2015). Comparative Study of Retinal Vessel Segmentation Based on Global Thresholding Techniques. Computational and Mathematical Methods in Medicine, 2015, 1–15[10.1155/2015/895267]
  14. 14.Ahsan Khawaja, Tariq M. Khan, Mohammad A. U. Khan, Syed Junaid Nawaz. (2019). A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation. Sensors, 19(22), 4949[10.3390/s19224949]
  15. 15.Zhitao Xiao, Xinpeng Zhang, Lei Geng, Fang Zhang, Jun Wu, Jun Tong, Philip O. Ogunbona, Chunyan Shan. (2017). Automatic non-proliferative diabetic retinopathy screening system based on color fundus image. BioMedical Engineering OnLine, 16(1), 122[10.1186/s12938-017-0414-z]
  16. 16.Temitope Mapayi, Jules-Raymond Tapamo, Serestina Viriri. (2015). Retinal Vessel Segmentation: A Comparative Study of Fuzzy C-Means and Sum Entropy Information on Phase Congruency. International Journal of Advanced Robotic Systems, 12(9)[10.5772/60581]
  17. 17.Dali Chen, Yingying Ao, Shixin Liu. (2020). Semi-Supervised Learning Method of U-Net Deep Learning Network for Blood Vessel Segmentation in Retinal Images. Symmetry, 12(7), 1067[10.3390/sym12071067]
  18. 18.K. Susheel Kumar, Nagendra Pratap Singh. (2022). Analysis of retinal blood vessel segmentation techniques: a systematic survey. Multimedia Tools and Applications, 82(5), 7679–7733[10.1007/s11042-022-13388-9]
  19. 19.Raheleh Kafieh, Hossein Rabbani, Fedra Hajizadeh, Mohammadreza Ommani. (2013). An Accurate Multimodal 3-D Vessel Segmentation Method Based on Brightness Variations on OCT Layers and Curvelet Domain Fundus Image Analysis. IEEE Transactions on Biomedical Engineering, 60(10), 2815–2823[10.1109/tbme.2013.2263844]
  20. 20.M. Islamuddin Ahmed, M. Ashraful Amin. (2012). High speed detection of optical disc in retinal fundus image. Signal Image and Video Processing, 9(1), 77–85[10.1007/s11760-012-0412-3]
  21. 21.Nagendra Pratap Singh, Rajeev Srivastava. (2018). Extraction of Retinal Blood Vessels by Using an Extended Matched Filter Based on Second Derivative of Gaussian. Proceedings of the National Academy of Sciences India Section A Physical Sciences, 89(2), 269–277[10.1007/s40010-017-0465-3]
  22. 22.Nagendra Pratap Singh, Rajeev Srivastava. (2016). Weibull Probability Distribution Function-Based Matched Filter Approach for Retinal Blood Vessels Segmentation. Advances in intelligent systems and computing, 427–437[10.1007/978-981-10-2525-9_40]
  23. 23.Yibin Tian, Ming Wen, Dajiang Lu, Xiaopin Zhong, Zongze Wu. (2024). Biological Basis and Computer Vision Applications of Image Phase Congruency: A Comprehensive Survey. Biomimetics, 9(7), 422[10.3390/biomimetics9070422]
  24. 24.Priya Rani, Priyadarshini N., Rajkumar E. R., Kumar Rajamani. (2016). Retinal vessel segmentation under pathological conditions using supervised machine learning. , 62–66[10.1109/icsmb.2016.7915088]
  25. 25.Mohsen Zardadi, Nasser Mehrshad, Seyyed Mohammad Razavi. (2016). Unsupervised Segmentation of Retinal Blood Vessels Using the Human Visual System Line Detection Model. , 4(2), 125–133[10.7508/jist.2016.02.008]
  26. 26.Shimaa S. Zeid, Raafat A. ElKamar, Shimaa I. Hassan. (2022). Fixed-Text vs. Free-Text Keystroke Dynamics for User Authentication. Engineering Research Journal - Faculty of Engineering (Shoubra), 51(1), 95–104[10.21608/erjsh.2022.224312]
  27. 27.Nagendra Pratap Singh, Vibhav Prakash Singh. (2020). Efficient Segmentation and Registration of Retinal Image Using Gumble Probability Distribution and BRISK Feature. Traitement du signal, 37(5), 855–864[10.18280/ts.370519]
  28. 28.Zhun Fan, Yibiao Rong, Jiewei Lu, Jiajie Mo, Fang Li, Xinye Cai, Tiejun Yang. (2016). Automated blood vessel segmentation in fundus image based on integral channel features and random forests. [10.1109/wcica.2016.7578606]
  29. 29.M. Islamuddin Ahmed, M. Ashraful Amin, Bruce Poon, Hong Yan. (2013). Retina based biometric authentication using phase congruency. International Journal of Machine Learning and Cybernetics, 5(6), 933–945[10.1007/s13042-013-0179-z]
  30. 30.Muhammad Amir Shafiq, Yazeed Alaudah, Ghassan AlRegib, Mohamed Deriche. (2017). Phase Congruency for image understanding with applications in computational seismic interpretation. , 1587–1591[10.1109/icassp.2017.7952424]
  31. 31.Marwah Abdullah Shlash, Imad Habeeb Obead. (2023). Supervised Classification of Groundwater Potential Mapping Using Integrated Machine Learning and GIS-Based Techniques. Mathematical Modelling and Engineering Problems, 10(3), 829–842[10.18280/mmep.100313]
  32. 32.Gehad Hassan, Aboul Ella Hassanien, Nashwa El-bendary, Ali Fahmy. (2015). Blood vessel segmentation approach for extracting the vasculature on retinal fundus images using Particle Swarm Optimization. , 11, 290–296[10.1109/icenco.2015.7416364]
  33. 33.Fa-Ling Yi, Wen-Hua Xu. (2012). Segmentation of blood vessels in color fundus images based on optimal multi-threshold method. , 28, 725–728[10.1109/icmlc.2012.6359014]
  34. 34.L. Cavinato, I. Fidone, M. Bacis, E. Del Sozzo, G. C. Durelli, M. D. Santambrogio. (2017). Software implementation and hardware acceleration of retinal vessel segmentation for diabetic retinopathy screening tests. , 2017, 1226–1229[10.1109/embc.2017.8037052]
  35. 35.Wani J. Morgan, Hsiao-Yeh Chu. (2023). An Unsupervised Vibration Noise Reduction Approach and Its Application in Lubrication Condition Monitoring. Lubricants, 11(2), 90[10.3390/lubricants11020090]
  36. 36.K Susheel Kumar, Nagendra Pratap Singh. (2022). Segmentation of retinal blood vessel using generalized extreme value probability distribution function(pdf)-based matched filter approach. Pattern Analysis and Applications, 26(1), 307–332[10.1007/s10044-022-01108-w]
  37. 37.Sreejini K S, V. K Govindan. (2015). A Review of Computer Aided Detection of Anatomical Structures and Lesions of DR from Color Retina Images. International Journal of Image Graphics and Signal Processing, 7(11), 55–69[10.5815/ijigsp.2015.11.08]
  38. 38.Temitope Mapayi, Serestina Viriri, Jules-Raymond Tapamo. (2015). Retinal vessel segmentation based on phase congruence and GLCM sum-entropy. , 1759–1764[10.1109/icit.2015.7125352]
  39. 39.Lukman Hakim, Novanto Yudistira, Muthusubash Kavitha, Takio Kurita. (2019). U-Net with Graph Based Smoothing Regularizer for Small Vessel Segmentation on Fundus Image. Communications in computer and information science, 515–522[10.1007/978-3-030-36802-9_55]
  40. 40.Nita Nimbarte, Milind Mushrif. (2016). Extraction of blood vessels and optic disc in retinal images. Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization, 6(1), 31–42[10.1080/21681163.2016.1154806]
  41. 41.Sushil Kumar saroj, Rakesh Kumar, Nagendra Pratap Singh. (2022). Retinal Blood Vessels Segmentation using Fréchet PDF and MSMO Method. ELCVIA Electronic Letters on Computer Vision and Image Analysis, 21(1), 27–46[10.5565/rev/elcvia.1453]
  42. 42.Aditya Gupta, Even Bringsdal, Nicole Salbuvik, Kristian Muri Knausgård, Morten Goodwin. (2022). An Accurate Convolutional Neural Networks Approach to Wound Detection for Farmed Salmon. Communications in computer and information science, 139–149[10.1007/978-3-031-08223-8_12]
  43. 43.Mohini Sharma, P.C Sau. (2019). Blood Vessel Segmentation using SegNet. , 620–624[10.1109/iscon47742.2019.9036307]
  44. 44.K. Susheel Kumar, Chandra Shekar Jatoth, Nagendra Pratap Singh. (2021). Segmentation of Retinal Blood Vessel Using an Algorithm-Based Gamma Distribution of Matched Filter. Algorithms for intelligent systems, 73–81[10.1007/978-981-33-6307-6_9]
  45. 45.K Susheel Kumar, Nagendra Pratap Singh. (2022). Retinal Blood Vessel Segmentation Using a Generalized Gamma Probability Distribution Function (PDF) of Matched Filtered. International Journal of Fuzzy System Applications, 11(2), 1–16[10.4018/ijfsa.296693]
  46. 46.M Islamuddin Ahmed, M Abdul Awal, M Ashraful Amin. (2012). Biometric authentication using circular segment around optical disc. , 1, 178–183[10.1109/iciev.2012.6317387]
  47. 47.Xiaolong Zhu, Borui Cao, Weihang Zhang, Huiqi Li. (2025). Adaptive multi-scale feature extraction and fusion network with deep supervision for retinal vessel segmentation. Multimedia Systems, 31(3)[10.1007/s00530-025-01789-3]
  48. 48.Chien-Cheng Lee, Shih-Che Ku. (2012). Blood vessel segmentation in retinal images based on the nonsubsampled contourlet transform. , 337–340[10.1109/isic.2012.6449775]
  49. 49.Joubin Nasehi Tehrani, Hong Yan, Meidong Zhu, C. Jin, A. L. McEwan. (2012). Measurement of retinal arteriolar diameters from auto scale phase congruency with fuzzy weighting and L1 Regularization. , 1, 1434–1437[10.1109/embc.2012.6346209]
  50. 50.Jyoti Sawant, Amol D. Vibhute. (2025). U-Net-based blood vessel segmentation using an improved data augmentation with green channel images. Procedia Computer Science, 260, 1071–1079[10.1016/j.procs.2025.03.292]
  51. 51.SathisKumar R. (2024). Enhanced Autonomous Speed Control System for Integrated Cars. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT, 08(05), 1–5[10.55041/ijsrem33627]
  52. 52.Xiaolong Zhu, Wenjian Li, Weihang Zhang, Jing Liu, Yue Qi, Qiuju Deng, Huiqi Li. (2025). A software for quantitative measurement of vessel parameters in fundus images. Computerized Medical Imaging and Graphics, 123, 102548[10.1016/j.compmedimag.2025.102548]
  53. 53.Abdelhafid Errabih, Abdessamad Benbah, Benayad Nsiri, Abdelalim Sadiq, My Hachem El Yousfi Alaoui, Rachid Oulad Haj Tham, Brahim Benaji. (2023). Diagnostic System and Classification of Diabetic Retinopathy Using Convolutional Neural Network. Lecture notes in electrical engineering, 597–611[10.1007/978-981-19-7753-4_45]
  54. 54.Avinash Bansal, Jan Kubíček, Jana Bahrová, Alice Varyšová, Martin Augustýnek, Marek Penhaker, Juraj Timkovič. (2026). Retinal Vessel Segmentation: A Comprehensive Review From Classical Methods to Deep Learning Advances (1982–2025). Advanced Intelligent Systems, 8(7)[10.1002/aisy.202501279]
  55. 55.V. R. Udupi, Subrahmanya K. N. (2015). Segmentation Methodologies for Retinal Structures: A Review.
  56. 56.Isshaa Aarya, Danchi Jiang. (2015). Automated and optimal detection of 3D articular cartilage using undecimated wavelets in MRI. Signal Image and Video Processing, 9(S1), 305–314[10.1007/s11760-015-0825-x]
  57. 57.K. Sivakami Sundari, Chandy Nagar. (2014). PRE PROCESSING OF VESSEL SEGMENTATION FOR THE IDENTIFICATION OF CARDIOVASCULAR DISEASES WITH RETINAL IMAGES.
  58. 58.Jaskirat Kaur, Deepti Mittal, Parveen Singla, Ramanpreet Kaur, Sharad Kumar Tiwari. (2023). Recognition of Hypertension Through Retinal Fundus Images by Segmentation of Blood Vasculature. Lecture notes in electrical engineering, 377–399[10.1007/978-981-99-2271-0_31]
  59. 59.T.D. Varsha Shree, M. Sakthisri, N. Sri Madhava Raja. (2019). Retinal Vasculature Segmentation and Analysis using Heuristic approach. 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), 2, 1–4[10.1109/icscan.2019.8878846]
  60. 60.Anju Rani, Deepti Mittal. (2016). Detection of Hypertensive Retinopathy throuh Color Fundus Images.
  61. 61.Ralibul Alam, Bruce Poon, Mahmood Kazi Mohammed, M. Ashraful Amin, Hong Yan. (2014). Annotating Retina Fundus Images for Teaching and Learning Diabetic Retinopathy Software. , 2(2), 62–67
  62. 62.Hakim, Lukman, Yudistira, Novanto, Kavitha, Muthusubash, Kurita, Takio. (2020). U-Net with Graph Based Smoothing Regularizer for Small Vessel Segmentation on Fundus Image. arXiv (Cornell University)[10.48550/arxiv.2009.07567]
  63. 63.Gehad Hassan, Aboul Ella Hassanien. (2017). A Review of Vessel Segmentation Methodologies and Algorithms. Advances in computational intelligence and robotics book series, 187–203[10.4018/978-1-5225-2229-4.ch009]
  64. 64.Gehad Hassan, Aboul Ella Hassanien. (2017). A Review of Vessel Segmentation Methodologies and Algorithms. IGI Global eBooks, 1–17[10.4018/978-1-5225-5195-9.ch001]
  65. 65.N. K. Adhish, R. Rajesh, T. M. Thasleema. (2019). A Hybrid Entropy Based Method Using Gaussian Kernel for Retinal Blood Vessel Segmentation. Advances in intelligent systems and computing, 272–279[10.1007/978-3-030-16657-1_25]
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