Skip to main content
Springer Nature Link
Log in

Even faster retinal vessel segmentation via accelerated singular value decomposition

  • Deep Learning & Neural Computing for Intelligent Sensing and Control
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Retinal blood vessel segmentation plays a vital role in medical image analysis since the appearance of vessels would contribute in the diagnosis, treatment, and evaluation for various diseases in ophthalmology and other fields, such as cardiology and neurosurgery. Among the state-of-the-art blood vessel segmentation techniques, the Hessian-based multi-scale filter has been widely used and shown its superior performance in the accuracy and visual effect. However, its execution time still remains a challenge due to the employment of eigenvalue decomposition in this approach. Bearing this in mind, we propose an accelerated matrix decomposition mechanism, which could be used to boost not only the original Hessian-based multi-scale approach but also the singular value decomposition-based algorithms. To evaluate the proposed method, we conducted comparison experiments between state-of-the-art techniques and our method. Experimental results show the superior performance of the proposed approach over state of the arts especially in execution time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Availability of data and material

We would like to share our image dataset very much with the public upon getting a permission from the hospital where the dataset was acquired. We will try our best to do it because we think it can facilitate the related fields growth and help on advertising our approach.

References

  1. Stanton AV, Wasan B, Cerutti A, Ford S, Marsh R, Sever PP, Thom SA, Hughes AD (1995) Vascular network changes in the retina with age and hypertension. J Hypertens 13(12 Pt 2):1724–1728

    Google Scholar 

  2. Wong TY, Klein R, Couper DJ, Cooper LS, Shahar E, Hubbard LD, Wofford MR, Sharrett AR (2001) Retinal microvascular abnormalities and incident stroke: the Atherosclerosis Risk in Communities Study. Lancet 358(9288):1134–1140

    Article  Google Scholar 

  3. Rahim SS, Jayne C, Palade V et al (2016) Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening. Neural Comput Appl 27(5):1149–1164

    Article  Google Scholar 

  4. Nipon T, Ittided P, Sansanee A, Direk P (2019) Hard exudate detection in retinal fundus images using supervised learning. Neural Comput Appl 1–18

  5. Hoover A, Kouznetsova V, Goldbaum M (1998) Locating blood vessels in retinal images by piece-wise threshold probing of a matched filter response. In: Proceedings of AMIA. Annual symposium. AMIA symposium, vol 19, no. 3, p 931

  6. Merlin M, Shan BP (2015) Robust and efficient segmentation of blood vessel in retinal images using gray-level textures features and fuzzy SVM. Biomed Pharmacol J 8(2):1111–1120

    Article  Google Scholar 

  7. Barkana BD, Saricicek I, Yildirim B (2016) Performance analysis of descriptive statistical features in retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier fusion. Knowl Based Syst 118:165–176

    Article  Google Scholar 

  8. Wang YB, Zhu CZ, Yan QF, Liu LQ (2017) A novel vessel segmentation in fundus images based on SVM. In: International conference on information system and artificial intelligence, pp 390–394

  9. Ghaderi R, Hassanpour H, Shahiri M (2007) Retinal vessel segmentation using the 2-D Morlet wavelet and neural network. In: International conference on intelligent and advanced systems, pp 1251–1255

  10. Rahebi J, Hardalaç F (2014) Retinal blood vessel segmentation with neural network by using gray-level co-occurrence matrix-based features. J Med Syst 38(8):85

    Article  Google Scholar 

  11. Sumathi T, Vivekanandan P, Balaji R (2017) Retinal vessel segmentation using neural network (RVSNN). IET Image Process

  12. Abdallah MB, Azar AT, Guedri H et al (2018) Noise-estimation-based anisotropic diffusion approach for retinal blood vessel segmentation. Neural Comput Appl 29(8):159–180

    Article  Google Scholar 

  13. Liu I, Sun Y (1993) Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme. IEEE Trans Med Imaging 12(2):334–341

    Article  Google Scholar 

  14. Yin Y, Adel M, Bourennane S (2012) Retinal vessel segmentation using a probabilistic tracking method. Pattern Recogn 45(4):1235–1244

    Article  MATH  Google Scholar 

  15. 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):263–9

    Article  Google Scholar 

  16. 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

    Article  Google Scholar 

  17. MendonA AM, 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

    Article  Google Scholar 

  18. Zana F, Klein JC (2001) Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans Image Process 10(7):1010

    Article  MATH  Google Scholar 

  19. Wang L, Bhalerao A, Wilson R (2007) Analysis of retinal vasculature using a multiresolution hermite model. IEEE Trans Med Imaging 26(2):137

    Article  Google Scholar 

  20. Narasimhaiyer H, Beach JM, 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

    Article  Google Scholar 

  21. Vincken KL, Frangi Alejandro F, Niessen Wiro J, Viergever MA (1998) Multiscale vessel enhancement filtering. In: International conference on medical image computing and computer-assisted intervention, pp 130–137

  22. Martinez-Perez ME, Hughes AD, Thom SA, Bharath AA, Parker KH (2007) Segmentation of blood vessels from red-free and fluorescein retinal images. Med Image Anal 11(1):47–61

    Article  Google Scholar 

  23. Fathi A, Naghshnilchi AR (2013) Integrating adaptive neuro-fuzzy inference system and local binary pattern operator for robust retinal blood vessels segmentation. Neural Comput Appl 22(1):163–174

    Article  Google Scholar 

  24. Rodrigues LC, Marengoni M (2017) Segmentation of optic disc and blood vessels in retinal images using wavelets, mathematical morphology and Hessian-based multi-scale filtering. Biomed Signal Process Control 36:39

    Article  Google Scholar 

  25. Shahid M, Taj IA (2018) Robust retinal vessel segmentation using vessel’s location map and Frangi enhancement filter. IET Image Process 12(4):494–501

    Article  Google Scholar 

  26. Ye DH, Kwon D (2008) Fast multiscale vessel enhancement filtering. In: Proceedings of SPIE—the international society for optical engineering, vol 6914, pp 691423–691423-8

  27. Dong X, Zhang H, Zhu L et al (2019) Weighted locality collaborative representation based on sparse subspace. J Vis Commun Image Represent 58:187–194

    Article  Google Scholar 

  28. Hu B, Wang H, Yu X et al (2017) Sparse network embedding for community detection and sign prediction in signed social networks. J Ambient Intell Humaniz Comput 1:1–12

    Google Scholar 

  29. Sun J, Wang Y, Li J et al (2018) View-invariant gait recognition based on kinect skeleton feature. Multimed Tools Appl 77:24909–24935

    Article  Google Scholar 

  30. Shi D, Zhu L, Cheng Z et al (2018) Unsupervised multi-view feature extraction with dynamic graph learning. J Vis Commun Image Represent 56:256–264

    Article  Google Scholar 

  31. Zong J, Meng L, Tan Y et al (2018) Adaptive reconstruction based multiple description coding with randomly offset quantizations. Multimed Tools Appl 77(8):1–21

    Google Scholar 

  32. Yanyun J, Yuanjie Z, Sujuan H et al (2017) Multimodal image alignment via linear mapping between feature modalities. J Healthc Eng 2017:1–6

    Article  Google Scholar 

  33. Zhao Y, Xu Z, Xiang Z et al (2017) Online learning of dynamic multi-view gallery for person re-identification. Multimed Tools Appl 76(1):217–241

    Article  Google Scholar 

  34. Sun J, Liu X, Wan W et al (2016) Video hashing based on appearance and attention features fusion via DBN. Neurocomputing 213:84–94

    Article  Google Scholar 

  35. Zhao M, Zhang H, Meng L (2016) An angle structure descriptor for image retrieval. China Commun 13(8):222–230

    Article  Google Scholar 

  36. Zhao M, Zhang H, Sun J (2016) A novel image retrieval method based on multi-trend structure descriptor. J Vis Commun Image Represent 38:73–81

    Article  Google Scholar 

  37. Liang C, Zhang H, Mei Q (2016) A discriminative feature extraction approach for tumor classification using gene expression data. Curr Bioinform 11(999):1–1

    Google Scholar 

  38. Ji H, Zhang H (2015) Analysis on the content features and their correlation of web pages for spam detection. China Commun 12(3):84–94

    Article  Google Scholar 

  39. Lei G, Yu-Fei W, Xin-Hua W (2018) Exploiting pre-trained network embeddings for recommendations in social networks. J Comput Sci Technol 33(4):682–696

    Article  Google Scholar 

  40. Li Z, Nie F, Chang X et al (2018) Rank-constrained spectral clustering with flexible embedding. IEEE Trans Neural Netw Learn Syst 29(12):6073–6082

    Article  MathSciNet  Google Scholar 

  41. Li J, Xu W, Wan W et al (2018) Movie recommendation based on bridging movie feature and user interest. J Comput Sci 26:128–134

    Article  Google Scholar 

  42. Zhang H, Ji H, Wang X (2012) Transfer learning from unlabeled data via neural networks. Neural Process Lett 36(2):173–187

    Article  Google Scholar 

  43. Wang Q, Zheng Y, Yang G et al (2017) Multi-scale rotation-invariant convolutional neural networks for lung texture classification. IEEE J Biomed Health Inform 22(1):184–195

    Article  Google Scholar 

  44. Dong X, Zhang H, Sun J et al (2017) A two-stage learning approach to face recognition. J Vis Commun Image Represent 43:21–29

    Article  Google Scholar 

  45. Sui X, Zheng Y, Wei B et al (2017) Choroid segmentation from optical coherence tomography with graph-edge weights learned from deep convolutional neural networks. Neurocomputing 237(C):332–341

    Article  Google Scholar 

  46. Zhang B, Zhu L, Sun J et al (2018) Cross-media retrieval with collective deep semantic learning. Multimed Tools Appl 7:1–20

    Google Scholar 

  47. Jian L, Hou S, Sui X et al (2018) Deblurring retinal optical coherence tomography via a convolutional neural network with anisotropic and double convolution layer. IET Comput Vis 12(6):900–907

    Article  Google Scholar 

  48. Zhang L, Luo M, Li Z et al (2019) Large-scale robust semisupervised classification. IEEE Trans Cybern 49(3):907–917

    Article  Google Scholar 

  49. Zhang C, Zhang H, Qiao J et al (2019) Deep transfer learning for intelligent cellular traffic prediction based on cross-domain big data. IEEE J Sel Areas Commun 99:1

    Google Scholar 

  50. Zhang M, Zhang H et al (2018) Supervised graph regularization based cross media retrieval with intra and inter-class correlation. J Vis Commun Image Represent 58:1–11

    Article  Google Scholar 

  51. Li W, Lei Z, Xiao D et al (2018) Joint feature selection and graph regularization for modality-dependent cross-modal retrieval. J Vis Commun Image Represent 54:213–222

    Article  Google Scholar 

  52. Yan J, Zhang H, Sun J et al (2017) Joint graph regularization based modality-dependent cross-media retrieval. Multimed Tools Appl 77:3009–3027

    Article  Google Scholar 

  53. Xing C, Liu MX, Cui QH et al (2012) Prediction of disease-related interactions between microRNAs and environmental factors based on a semi-supervised classifier. PLoS ONE 7(8):e43425

    Article  Google Scholar 

  54. Jian M, Zhang W, Yu H et al (2018) Saliency detection based on directional patches extraction and principal local color contrast. J Vis Commun Image Represent 57:1–11

    Article  Google Scholar 

  55. Jian M, Zhao R, Sun X et al (2018) Saliency detection based on background seeds by object proposals and extended random walk. J Vis Commun Image Represent 57:202–211

    Article  Google Scholar 

  56. Wu K, Simon HD (2000) Thick-restart Lanczos method for large symmetric eigenvalue problems. SIAM J Matrix Anal Appl 22(2):602–616

    Article  MathSciNet  MATH  Google Scholar 

  57. Staal J, Abràmoff MD, Niemeijer M, Viergever MA, Van Ginneken B (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501–509

    Article  Google Scholar 

  58. Hoover AW, Kouznetsova VL, Goldbaum MH (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging 19(3):203–210

    Article  Google Scholar 

  59. Zhao Y, Rada L, Chen K, Harding SP, Zheng Y (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

    Article  Google Scholar 

  60. Azzopardi G, Strisciuglio N, Vento M, Petkov N (2015) Trainable COSFIRE filters for vessel delineation with application to retinal images. Med Image Anal 19(1):46–57

    Article  Google Scholar 

  61. Zhu C, Zou B, Zhao R, Cui J, Duan X, Chen Z, Liang Y (2016) Retinal vessel segmentation in colour fundus images using extreme learning machine. Comput Med Imaging Graph 55:68

    Article  Google Scholar 

  62. Aslani S, Sarnel H (2016) A new supervised retinal vessel segmentation method based on robust hybrid features. Biomed Signal Process Control 30:1–12

    Article  Google Scholar 

  63. He Y, Zheng Y, Zhao Y, Ren Y, Lian J, Gee J (2017) Retinal image denoising via bilateral filter with a spatial kernel of optimally oriented line spread function. Comput Math Methods Med 2017(3, article 67):1769834

    Google Scholar 

  64. Lam BSY, Gao Y, Liew WC (2010) General retinal vessel segmentation using regularization-based multiconcavity modeling. IEEE Trans Med Imaging 29(7):1369–81

    Article  Google Scholar 

  65. Zhao Y, Liu Y, Wu X, Harding SP, Zheng Y (2015) Retinal vessel segmentation: an efficient graph cut approach with retinex and local phase. PLoS ONE 10(4):e0122332

    Article  Google Scholar 

  66. Tian J, Zhang H, Wu D et al (2018) QoS-constrained medium access probability optimization in wireless interference-limited networks. IEEE Trans Commun 66(3):1064–1077

    Article  Google Scholar 

  67. Ji C, Zhao C, Pan L et al (2019) A just-in-time shapelet selection service for online time series classification. Comput Netw 157:89–98

    Article  Google Scholar 

  68. Zheng X, Tian J, Xiao X et al (2019) A heuristic survivable virtual network mapping algorithm. Soft Comput 23(5):1453–1463

    Article  Google Scholar 

  69. Xiao X, Zheng X, Zhang Y et al (2017) A multidomain survivable virtual network mapping algorithm. Secur Commun Netw 2017:1–12

    Article  Google Scholar 

  70. Zheng XW, Hu B, Lu DJ et al (2017) Energy-efficient virtual network embedding in networks for cloud computing. Int J Web Grid Serv 13(1):75–93

    Article  Google Scholar 

  71. Zhang T (2016) Delay-aware data transmission of multi-carrier communications in the presence of renewable energy. Wirel Person Commun 90(3):1533–1545

    Article  Google Scholar 

  72. Zheng X, Hu B, Lu D, Liu H (2016) A multi-objective virtual network embedding algorithm in cloud computing. J Internet Technol 17:633–642

    Google Scholar 

  73. Shao Z, Li L, Lin J (2015) Research on two-layer heterogeneous wireless control network framework at airport with silent mechanism. Int J Sens Netw 17(4):243–248

    Article  Google Scholar 

  74. Zhang T, Chen W, Cao Z et al (2015) DNF–AF selection two-way relaying. Wireless Pers Commun 80(2):805–818

    Article  Google Scholar 

  75. Lu D, Huang X, Zhang W et al (2014) Interference-aware spectrum handover for cognitive radio networks. Wirel Commun Mobile Comput 14:1099–1112

    Article  Google Scholar 

  76. Zhai L, Zhang X, Xie G et al (2012) Performance analysis of non-saturated IEEE 802.11 DCF networks. IEICE Trans Commun 95(7):2509–2512

    Article  Google Scholar 

  77. Zhai L, Zhang X (2012) Modified 802.11-based opportunistic spectrum access in cognitive radio networks. ETRI J 34(2):276–279

    Article  Google Scholar 

  78. Zhai L, Xie G (2011) A slot-based opportunistic spectrum access for cognitive radio networks. IEICE Trans Commun 94(11):3183–3185

    Article  Google Scholar 

  79. Liu Y, Wang H, Li T et al (2016) Attribute-based handshake protocol for mobile healthcare social networks. Future Gener Comput Syst 86:873–880

    Article  Google Scholar 

  80. Linbo Z, Hua W, Chuangen G et al (2016) A spectrum access based on quality of service (QoS) in cognitive radio networks. PLoS ONE 11(5):e0155074

    Article  Google Scholar 

  81. Du T, Qu S, Liu F et al (2015) An energy efficiency semi-static routing algorithm for WSNs based on HAC clustering method. Inf Fusion 21:18–29

    Article  Google Scholar 

  82. Hong J, Li Z, Lu D et al (2013) Sleeping schedule-aware local broadcast in wireless sensor networks. Int J Distrib Sens Netw. https://doi.org/10.1155/2013/451970

  83. Zhang W, Rijmen V (2019) Division cryptanalysis of block ciphers with a binary diffusion layer. IET Inf Secur 13(2):87–95

    Article  Google Scholar 

  84. Han G, Zhang W, Zhao H et al (2019) An upper bound of the longest impossible differentials of several block ciphers. KSII Trans Internet Inf Syst 13(1):435–451

    Google Scholar 

  85. Yan YX, Wu L, Xu WY et al (2019) Integrity audit of shared cloud data with identity tracking. Secur Commun Netw 2019:1–11

    Article  Google Scholar 

  86. Zhang P, Zhang W (2018) Differential cryptanalysis on block cipher skinny with MILP program. Secur Commun Netw 2018:1–11

    Google Scholar 

  87. Wang H, He D, Shen J et al (2017) Verifiable outsourced ciphertext-policy attribute-based encryption in cloud computing. Soft Comput 21(24):7325–7335

    Article  MATH  Google Scholar 

  88. Wang H, Zheng Z, Wu L et al (2017) New directly revocable attribute-based encryption scheme and its application in cloud storage environment. Clust Comput 20(3):2385–2392

    Article  Google Scholar 

  89. Han G, Zhang W (2017) Improved biclique cryptanalysis of the lightweight block cipher piccolo. Secur Commun Netw 2017:1–12

    Google Scholar 

  90. Zhang W, Zhang W (2016) Algebraic techniques on searching linear diffusion layers in block cipher. Secur Commun Netw 9(17):4285–4294

    Article  Google Scholar 

  91. Zhang WY, Li YY, Wu L (2012) A new one-bit difference collision attack on HAVAL-128. Sci China Inf Sci 55(11):2521–2529

    Article  MathSciNet  MATH  Google Scholar 

  92. Hong L, Xu B, Lu D et al (2018) A path planning approach for crowd evacuation in buildings based on improved artificial bee colony algorithm. Appl Soft Comput 68:360–376

    Article  Google Scholar 

  93. Xin Q, Hong L, Hao Z et al (2018) A collective motion model based on two-layer relationship mechanism for bi-direction pedestrian flow simulation. Simul Model Pract Theory 84:268–285

    Article  Google Scholar 

  94. Liu H, Liu B, Zhang H et al (2018) Crowd evacuation simulation approach based on navigation knowledge and two-layer control mechanism. Inf Sci 436–437:247–267

    Article  MathSciNet  Google Scholar 

  95. Han Y, Hong L, Moore P (2017) Extended route choice model based on available evacuation route set and its application in crowd evacuation simulation. Simul Model Pract Theory 75:1–16

    Article  Google Scholar 

  96. Zhai L, Wang H, Liu C et al (2017) Distributed schemes for crowdsourcing-based sensing task assignment in cognitive radio networks. Wirel Commun Mobile Comput 2017:1–8

    Google Scholar 

  97. Yu Y, Du L, Zeng C et al (2016) Automatic localization method of small casting defect based on deep learning feature. Chin J Sci Instrum 37:1364–1370

    Google Scholar 

  98. Birlutiu A, Burlacu A, Kadar M et al (2017) Defect detection in porcelain industry based on deep learning techniques 2017 19th international symposium on symbolic and numeric algorithms for scientific computing (SYNASC)

  99. Lien PC, Zhao Q (2018) Product surface defect detection based on deep learning. In: 2018 IEEE 16th international conference on dependable, autonomic and secure computing, 16th international conference on pervasive intelligence and computing, 4th international conference on big data intelligence and computing and cyber science and technology congress (DASC/PiCom/DataCom/CyberSciTech)

  100. Huang D, Lin C, Chen C et al (2018) The Internet technology for defect detection system with deep learning method in smart factory. Int Conf Inf Manag 2018:98–102. https://doi.org/10.1109/infoman.2018.8392817

    Article  Google Scholar 

  101. Wang M, Cheng JCP (2018) Development and improvement of deep, learning based automated defect, detection for sewer pipe inspection, using faster R-CNN. Advanced Computing Strategies for Engineering, pp. 171–192

  102. Lu D, Hu B, Zheng X et al (2017) Session-based cloud video delivery networks in mobile internet. J Internet Technol 18(7):1561–1571

    Google Scholar 

Download references

Acknowledgements

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

Funding

This is one of the achievements of enterprise innovation management research institute.

Author information

Authors and Affiliations

  1. College of Industry and Commerce, Shandong Management University, Jinan, 250357, China

    Yan Zhang

  2. School of Light Industry Science and Engineering, Qilu University of Technology (Shandong Academy of Science), Jinan, 250353, China

    Luo Rong

  3. Department of Electrical Engineering Information Technology, Shandong University of Sci&Tech, Jinan, 250031, China

    Yan Zhang, Jian Lian & Chengjiang Li

  4. School of Information Science and Engineering, Key Lab of Intelligent Computing and Information Security in Universities of Shandong, Institute of Life Sciences, Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, and Key Lab of Intelligent Information Processing, Shandong Normal University, Jinan, 250358, China

    Jian Lian, Weikuan Jia & Yuanjie Zheng

Authors
  1. Yan Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jian Lian
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Luo Rong
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Weikuan Jia
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Chengjiang Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Yuanjie Zheng
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

All authors take part in the discussion of the work described in this paper.

Corresponding author

Correspondence to Jian Lian.

Ethics declarations

Conflict of interest

There are no potential competing interests in our paper. And all authors have seen the manuscript and approved to submit to your journal. We confirm that the content of the manuscript has not been published or submitted for publication elsewhere.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Lian, J., Rong, L. et al. Even faster retinal vessel segmentation via accelerated singular value decomposition. Neural Comput & Applic 32, 1893–1902 (2020). https://doi.org/10.1007/s00521-019-04505-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04505-1

Keywords