Skip to main content
SpringerLink
Log in

A novel methodology for vessel extraction from retinal fundus image and detection of neovascularization

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Vessel extraction from the retinal fundus images plays a significant role in ophthalmologic disease diagnosis. Proliferative Diabetic Retinopathy (PDR) is the ultimate stage of Diabetic Retinopathy where proliferation of new and fragile blood vessels grow in human retina. These new blood vessels often show a tendency to rupture which further leads to severe damage of human eye. Neovascularization at the disk (NVD) and elsewhere (NVE) are the two general categories of PDR. So, disease diagnosis at the early stage by detecting the newly generated thin vessels demands utmost importance. Literature witness that most of the existing works emphasised on detecting only NVD. The goal of this work is to detect NVD along with NVE, as both the stages are equally devastating. The disease detection requires the extraction of vessels for subsequent analysis. A novel vessel extraction methodology has been proposed here which is capable of extracting the thick and thin vessels for further analysis. The experimental results have been tested and verified with two publicly available datasets of retinal fundus images, DRIVE and STARE. Finally, experiment for NVD and NVE detection has been carried out with DIARET-DB1 data-set. Comparison of performance with some other state-of-the-works shows superiority of the proposed methodology.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Agurto C, Yu H, Murray V, Pattichis MS, Barriga S, Bauman W, Soliz P (2012) Detection of neovascularization in the optic disc using an am-fm representation, granulometry, and vessel segmentation. In: 2012 Annual international conference of the IEEE engineering in medicine and biology society, IEEE, pp 4946–4949

  2. Akram MU, Tariq A, Khan SA (2012) Detection of neovascularization for screening of proliferative diabetic retinopathy. In: International conference image analysis and recognition, Springer, pp 372–379

  3. Al-Diri B, Hunter A, Steel D (2009) An active contour model for segmenting and measuring retinal vessels. IEEE Trans Medical Imaging 28(9):1488–1497

    Article  Google Scholar 

  4. Azzopardi G, Strisciuglio N, Vento M, Petkov N (2015) Trainable cosfire filters for vessel delineation with application to retinal images. Medical Image Analysis 19(1):46–57

    Article  Google Scholar 

  5. Cinsdikici MG, Aydın D (2009) Detection of blood vessels in ophthalmoscope images using mf/ant (matched filter/ant colony) algorithm. Computer Methods and Programs in Biomedicine 96(2):85–95

    Article  Google Scholar 

  6. Dash J, Bhoi N (2017) A thresholding based technique to extract retinal blood vessels from fundus images. Future Computing and Informatics Journal 2 (2):103–109

    Article  Google Scholar 

  7. Daxer A (1993) The fractal geometry of proliferative diabetic retinopathy: implications for the diagnosis and the process of retinal vasculogenesis. Current Eye Research 12(12):1103–1109

    Article  Google Scholar 

  8. Dong Y, Ren W, Zhang K (2019) Deep supervision adversarial learning network for retinal vessel segmentation. In: 2019 12Th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), IEEE, pp 1–6

  9. Firdausy K, Wahyunggoro O, Nugroho HA, Sasongko MB (2019) A study on recent developments for detection of neovascularization. In: 2019 11Th international conference on information technology and electrical engineering (ICITEE), IEEE, pp 1–6

  10. Wilfred Franklin S, Edward Rajan S (2014) Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images. Biocybernetics and Biomedical Engineering 34(2):117–124

    Article  Google Scholar 

  11. Fraz MM, Barman SA, Remagnino P, Hoppe A, Basit A, Uyyanonvara B, Rudnicka AR, Owen CG (2012) An approach to localize the retinal blood vessels using bit planes and centerline detection. Computer Methods and Programs in Biomedicine 108(2):600–616

    Article  Google Scholar 

  12. Frucci M, Riccio D, di Baja GS, Serino L (2016) Severe: Segmenting vessels in retina images. Pattern Recogn Lett 82:162–169

    Article  Google Scholar 

  13. Garhöfer G, Zawinka C, Resch H, Huemer KH, Schmetterer L, Dorner GT (2004) Response of retinal vessel diameters to flicker stimulation in patients with early open angle glaucoma. Journal of Glaucoma 13(4):340–344

    Article  Google Scholar 

  14. Guo C, Szemenyei M, Yi Y, Xue Y, Zhou W, Li Y (2020) Dense residual network for retinal vessel segmentation. In: ICASSP 2020-2020 IEEE International conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 1374–1378

  15. Hassan SSA, Bong DBL, Premsenthil M (2012) Detection of neovascularization in diabetic retinopathy. Journal of Digital Imaging 25(3):437–444

    Article  Google Scholar 

  16. Holzinger A, Langs G, Denk H, Zatloukal K, Müller H (2019) Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 9(4):e1312

    Google Scholar 

  17. Hoover AD, Kouznetsova V, Goldbaum M (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Medical Imaging 19(3):203–210

    Article  Google Scholar 

  18. Huang H, Ma H, Qian W (2019) Automatic parallel detection of neovascularization from retinal images using ensemble of extreme learning machine. In: 2019 41St annual international conference of the IEEE engineering in medicine and biology society (EMBC), IEEE, pp 4712–4716

  19. Imani E, Javidi M, Pourreza H-R (2015) Improvement of retinal blood vessel detection using morphological component analysis. Computer Methods and Programs in Biomedicine 118(3):263–279

    Article  Google Scholar 

  20. Jiang X, Mojon D (2003) Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Trans Pattern Anal Mach Intel 25(1):131–137

    Article  Google Scholar 

  21. Joussen AM, Poulaki V, Le ML, Koizumi K, Esser C, Janicki H, Schraermeyer U, Kociok N, Fauser S, Kirchhof B et al (2004) A central role for inflammation in the pathogenesis of diabetic retinopathy. The FASEB Journal 18(12):1450–1452

    Article  Google Scholar 

  22. Kauppi T, Kalesnykiene V, Kamarainen J-K, Lensu L, Sorri I, Raninen A, Voutilainen R, Uusitalo H, Kälviäinen H, Pietilä J (2007) The diaretdb1 diabetic retinopathy database and evaluation protocol. In: BMVC, vol 1, pp 1–10

  23. Kowluru RA, Tang J, Kern TS (2001) Abnormalities of retinal metabolism in diabetes and experimental galactosemia: Vii. effect of long-term administration of antioxidants on the development of retinopathy. Diabetes 50(8):1938–1942

    Article  Google Scholar 

  24. Kromm C, Rohr K (2020) Inception capsule network for retinal blood vessel segmentation and centerline extraction. In: 2020 IEEE 17Th international symposium on biomedical imaging (ISBI), IEEE, pp 1223–1226

  25. Kushol Rafsanjany, Kabir Md, Sirajus Salekin Md, Ashikur Rahman ABM (2017) Contrast enhancement by top-hat and bottom-hat transform with optimal structuring element: Application to retinal vessel segmentation, pp 533–540, 07

  26. Lam BSY, Yan H (2008) A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields. IEEE Trans Med Imaging 27(2):237–246

    Article  Google Scholar 

  27. Lee J, Zee BCY, Li Q (2013) Detection of neovascularization based on fractal and texture analysis with interaction effects in diabetic retinopathy. PloS One 8(12):e75699

    Article  Google Scholar 

  28. Li Q, You J, Zhang D (2012) Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses. Expert Syst Appl 39(9):7600–7610

    Article  Google Scholar 

  29. Marín D, Aquino A, Gegúndez-Arias ME, Bravo JM (2010) A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans Medical Imaging 30 (1):146–158

    Article  Google Scholar 

  30. Elena Martinez-Perez M, Hughes AD, Thom SA, Bharath AA, Parker KH (2007) Segmentation of blood vessels from red-free and fluorescein retinal images. Medical Image Analysis 11(1):47–61

    Article  Google Scholar 

  31. Mendonca AM, Campilho A (2006) Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans Medical Imaging 25(9):1200–1213

    Article  Google Scholar 

  32. Miri MS, Mahloojifar A (2010) Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction. IEEE Trans Biomed Eng 58(5):1183–1192

    Article  Google Scholar 

  33. Mitchell P, Leung H, Wang JJ, Rochtchina E, Lee AJ, Wong TY, Klein R (2005) Retinal vessel diameter and open-angle glaucoma: the blue mountains eye study. Ophthalmology 112(2):245–250

    Article  Google Scholar 

  34. Mudigonda S, Oloumi F, Katta KM, Rangayyan RM (2015) Fractal analysis of neovascularization due to diabetic retinopathy in retinal fundus images. In: 2015 E-Health and Bioengineering Conference (EHB), IEEE, pp 1–4

  35. Nagel E, Vilser W, Lanzi IM (2001) Retinal vessel reaction to short-term iop elevation in ocular hypertensive and glaucoma patients. European Journal of Ophthalmology 11(4):338–344

    Article  Google Scholar 

  36. Narasimhan K, Neha VC, Vijayarekha K (2012) Hypertensive retinopathy diagnosis from fundus images by estimation of avr. Procedia Eng 38:980–993

    Article  Google Scholar 

  37. Niemeijer M, Staal J, van Ginneken B, Loog M, Abramoff MD (2004) Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Medical imaging 2004: image processing, vo 5370, International Society for Optics and Photonics, pp 648–656

  38. Ohno T, Takamoto S, Ando J, Morita T, Fujita H, Hirata Y, Shigeeda T, Hirose A, Nagai R (2007) Diabetic retinopathy and coronary implantation of sirolimus-eluting stents. J Interv Cardiol 20(2):122–131

    Article  Google Scholar 

  39. Oloumi F, Rangayyan RM, Casti P, Ells AL (2015) Computer-aided diagnosis of plus disease via measurement of vessel thickness in retinal fundus images of preterm infants. Comput Bio Medic 66:316–329

    Article  Google Scholar 

  40. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

    Article  Google Scholar 

  41. Rahim SS, Palade V, Almakky I, Holzinger A (2019) Detection of diabetic retinopathy and maculopathy in eye fundus images using deep learning and image augmentation. In: International cross-domain conference for machine learning and knowledge extraction, Springer, pp 114–127

  42. Rahim SS, Palade V, Jayne C, Holzinger A, Shuttleworth J (2015) Detection of diabetic retinopathy and maculopathy in eye fundus images using fuzzy image processing. In: International conference on brain informatics and health, Springer, pp 379–388

  43. Prasad Reddy PVGD (2020) Blood vessel extraction in fundus images using hessian eigenvalues and adaptive thresholding. Evol Intel, pp 1–6

  44. Ricci E, Perfetti R (2007) Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans Medical Imaging 26(10):1357–1365

    Article  Google Scholar 

  45. Rodrigues J, Bezerra N (2016) Retinal vessel segmentation using parallel grayscale skeletonization algorithm and mathematical morphology. In: 2016 29Th SIBGRAPI conference on graphics, patterns and images (SIBGRAPI), IEEE, pp 17–24

  46. Roychowdhury S, Koozekanani DD, Parhi KK (2014) Blood vessel segmentation of fundus images by major vessel extraction and subimage classification. IEEE Journal of Biomedical and Health Informatics 19(3):1118–1128

    Google Scholar 

  47. Roychowdhury S, Koozekanani DD, Parhi KK (2016) Automated detection of neovascularization for proliferative diabetic retinopathy screening. In: 2016 38Th annual international conference of the IEEE engineering in medicine and biology society (EMBC), IEEE, pp 1300–1303

  48. Saranya K, Ramasubramanian B, Kaja Mohideen S (2012) A novel approach for the detection of new vessels in the retinal images for screening diabetic retinopathy. In: 2012 International conference on communication and signal processing, IEEE, pp 57–61

  49. Soares JVB, Leandro JJG, Cesar RM, Jelinek HF, Cree MJ (2006) Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification. IEEE Trans Medical Imaging 25(9):1214–1222

    Article  Google Scholar 

  50. 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 

  51. Triwijoyo BK, Pradipto YD (2017) Detection of hypertension retinopathy using deep learning and boltzmann machines. J Phys Conf Ser 801:1–7

    Article  Google Scholar 

  52. Valsania P, Warram JH, Rand LI, Krolewski AS (1993) Different determinants of neovascularization on the optic disc and on the retina in patients with severe nonproliferative diabetic retinopathy. Arch Ophthalmol 111 (2):202–206

    Article  Google Scholar 

  53. Wahid Fa, Raju G (2019) A dual step strategy for retinal thin vessel enhancement/extraction. In: 2019 Amity international conference on artificial intelligence (AICAI), IEEE, pp 666–671

  54. Wankhede PR, Khanchandani KB (2015) Retinal blood vessel segmentation using graph cut analysis. In: 2015 International conference on industrial instrumentation and control (ICIC), IEEE, pp 1429–1432

  55. Wu Y, Xia Y, Song Y, Zhang Y, Cai W (2018) Multiscale network followed network model for retinal vessel segmentation. In: International conference on medical image computing and computer-assisted intervention, Springer, pp 119–126

  56. Yu S, Di X, Kanagasingam Y (2017) Machine learning based automatic neovascularization detection on optic disc region. IEEE Journal of Biomedical and Health Informatics 22(3):886–894

    Article  Google Scholar 

  57. Zana F, Klein J-C (2001) Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans Image Process 10 (7):1010–1019

    Article  Google Scholar 

  58. Zhang B, Zhang L, Zhang L, Karray F (2010) Retinal vessel extraction by matched filter with first-order derivative of gaussian. Comput Bio Med 40(4):438–445

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India

    Sayan Das & Sanjoy Kumar Saha

  2. Department of Computer Science and Engineering, Institute of Engineering & Management, Kolkata, West Bengal, India

    Nilanjana Dutta Roy

  3. Department of Information Technology, Indian Institute of Engineering Science & Technology, Shibpur, Howrah, India

    Arindam Biswas

Authors
  1. Sayan Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nilanjana Dutta Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arindam Biswas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sanjoy Kumar Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sayan Das.

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

Das, S., Roy, N.D., Biswas, A. et al. A novel methodology for vessel extraction from retinal fundus image and detection of neovascularization. Multimed Tools Appl 80, 4093–4110 (2021). https://doi.org/10.1007/s11042-020-09889-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09889-0

Keywords

Search

Navigation