Skip to main content
Top
Published in:

24-11-2023 | Technical Paper

Fundus vessel structure segmentation based on Bel-Hat transformation

Authors: Rajat Suvra Nandy, Rohit Kamal Chatterjee, Abhishek Das

Published in: Microsystem Technologies | Issue 4/2024

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Retinal diseases such as Diabetic Retinopathy (DR), Hypertensive Retinopathy (HR), different types of Occlusions, etc., are associated with the deformity observed in the Retinal Vessel Structure (RVS). This paper proposes an automatic unsupervised vessel segmentation technique to separate the RVS with insignificant change in curvature of the vessel and eliminate the noises from the vessel structure and the background. The method involves three phases: preprocessing, where the fundus image is enhanced based on local information, and the noises are separated from the vessels. The second phase introduces a unique Bel–Hat transformation, which simultaneously uses two different groups of Structural Elements: the Neighbor Adaptive Line Structuring Element (NALSE) and the 2D Gaussian Structuring Element (2DGSE). These combined groups of Structural Elements can separate the vessel structure from the background by changing the size and orientation of the Structural Elements. Lastly, a novel robust statistical threshold is used, based on the statistical distribution of the area of the isolated objects, to segment the accurate noise-free Retinal Vessel Structure (RVS). This proposed method is more accurate than the recently proposed unsupervised and supervised methods.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literature
go back to reference Abràmoff MD, Garvin MK, Sonka M (2010) Retinal imaging and image analysis. IEEE Trans Med Imaging 1(3):169–208 Abràmoff MD, Garvin MK, Sonka M (2010) Retinal imaging and image analysis. IEEE Trans Med Imaging 1(3):169–208
go back to reference Almotiri J, Elleithy K, Elleithy A (2018) Retinal vessels segmentation techniques and algorithms: a survey. Appl Sci 8(2):155CrossRef Almotiri J, Elleithy K, Elleithy A (2018) Retinal vessels segmentation techniques and algorithms: a survey. Appl Sci 8(2):155CrossRef
go back to reference Alom MZ, Yakopcic C, Hasan M, Taha TM, Asari VK (2019) Recurrent residual U-Net for medical image segmentation. J Med Imag 6(01):1CrossRef Alom MZ, Yakopcic C, Hasan M, Taha TM, Asari VK (2019) Recurrent residual U-Net for medical image segmentation. J Med Imag 6(01):1CrossRef
go back to reference Annunziata R, Garzelli A, Ballerini L, Mecocci A, Trucco E (2016) Leveraging multiscale hessian-based enhancement with a novel exudate inpainting technique for retinal vessel segmentation. IEEE J Biomed Health Inform 20(4):1129–1138CrossRef Annunziata R, Garzelli A, Ballerini L, Mecocci A, Trucco E (2016) Leveraging multiscale hessian-based enhancement with a novel exudate inpainting technique for retinal vessel segmentation. IEEE J Biomed Health Inform 20(4):1129–1138CrossRef
go back to reference Aslani S, Sarnel H (2016) A new supervised retinal vessel segmentation method based on robust hybrid features. Biomed Signal Process Control 30:1–12CrossRef Aslani S, Sarnel H (2016) A new supervised retinal vessel segmentation method based on robust hybrid features. Biomed Signal Process Control 30:1–12CrossRef
go back to reference Aurangzeb K, Alharthi RS, Haider SI, Alhussein M (2022) An Efficient and Light Weight Deep Learning Model for Accurate Retinal Vessels Segmentation. IEEE Access. 11:23107–23118CrossRef Aurangzeb K, Alharthi RS, Haider SI, Alhussein M (2022) An Efficient and Light Weight Deep Learning Model for Accurate Retinal Vessels Segmentation. IEEE Access. 11:23107–23118CrossRef
go back to reference Azzopardi G, Petkov N (2013) Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters. Pattern Recogn Lett 34(8):922–933CrossRef Azzopardi G, Petkov N (2013) Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters. Pattern Recogn Lett 34(8):922–933CrossRef
go back to reference Bibiloni P, González-Hidalgo M, Massanet S (2019) A realtime fuzzy morphological algorithm for retinal vessel segmentation. J Real-Time Image Proc 16:2337–2350CrossRef Bibiloni P, González-Hidalgo M, Massanet S (2019) A realtime fuzzy morphological algorithm for retinal vessel segmentation. J Real-Time Image Proc 16:2337–2350CrossRef
go back to reference Burt PJ, Adelson EH (1983) The Laplacian pyramid as a compact image code. IEEE Trans Commun 31(4):532–540CrossRef Burt PJ, Adelson EH (1983) The Laplacian pyramid as a compact image code. IEEE Trans Commun 31(4):532–540CrossRef
go back to reference 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):263–269CrossRef 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):263–269CrossRef
go back to reference Cheng E, Du L, Wu Y, Zhu YJ, Megalooikonomou V, Ling H (2014) Discriminative vessel segmentation in retinal images by fusing context aware hybrid features. Mach vis Appl 25(7):1779–1792CrossRef Cheng E, Du L, Wu Y, Zhu YJ, Megalooikonomou V, Ling H (2014) Discriminative vessel segmentation in retinal images by fusing context aware hybrid features. Mach vis Appl 25(7):1779–1792CrossRef
go back to reference Deari S, Oksuz I, Ulukaya S (2023) Block Attention and Switchable Normalization Based Deep Learning Framework for Segmentation of Retinal Vessels. IEEE Access 11:38263–38274CrossRef Deari S, Oksuz I, Ulukaya S (2023) Block Attention and Switchable Normalization Based Deep Learning Framework for Segmentation of Retinal Vessels. IEEE Access 11:38263–38274CrossRef
go back to reference Fan Z, Lu J, Wei C, Huang H, Cai X, Chen X (2019) A Hierarchical Image Matting Model for Blood Vessel Segmentation in Fundus Images. IEEE Trans Image Process 28(5):2367–2377MathSciNetCrossRef Fan Z, Lu J, Wei C, Huang H, Cai X, Chen X (2019) A Hierarchical Image Matting Model for Blood Vessel Segmentation in Fundus Images. IEEE Trans Image Process 28(5):2367–2377MathSciNetCrossRef
go back to reference Fan Z, Mo J (2016) Automated blood vessel segmentation based on de-noising auto-encoder and neural network. In: Proceedings of the International Conference on Machine Learning and Cybernetics, vol. 2. IEEE, pp 849–856 Fan Z, Mo J (2016) Automated blood vessel segmentation based on de-noising auto-encoder and neural network. In: Proceedings of the International Conference on Machine Learning and Cybernetics, vol. 2. IEEE, pp 849–856
go back to reference Farnell DJ, Hatfield F, Knox P, Reakes M, Spencer S, Parry D, Harding S (2008) Enhancement of blood vessels in digital fundus photographs via the application of multiscale line operators. J Frankl Inst 345:748–765CrossRef Farnell DJ, Hatfield F, Knox P, Reakes M, Spencer S, Parry D, Harding S (2008) Enhancement of blood vessels in digital fundus photographs via the application of multiscale line operators. J Frankl Inst 345:748–765CrossRef
go back to reference Fathi A, Naghsh-Nilchi AR (2014) General rotation-invariant local binary patterns operator with application to blood vessel detection in retinal images. Pattern Anal Appl 17(1):69–81MathSciNetCrossRef Fathi A, Naghsh-Nilchi AR (2014) General rotation-invariant local binary patterns operator with application to blood vessel detection in retinal images. Pattern Anal Appl 17(1):69–81MathSciNetCrossRef
go back to reference Fattal R (2009) Edge-avoiding wavelets and their applications. A.C.M. Transactions on Graphics (Proc. SIGGRAPH) 28. 3 Fattal R (2009) Edge-avoiding wavelets and their applications. A.C.M. Transactions on Graphics (Proc. SIGGRAPH) 28. 3
go back to reference Franklin SW, Raja SE (2014) Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal Images. Biocybern Biomed Eng 34:117–124CrossRef Franklin SW, Raja SE (2014) Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal Images. Biocybern Biomed Eng 34:117–124CrossRef
go back to reference Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG, Barman SA (2012) An ensemble classification based approach applied to retinal blood vessel segmentation. IEEE Trans Biomed Eng 59(9):2538–2548CrossRef Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG, Barman SA (2012) An ensemble classification based approach applied to retinal blood vessel segmentation. IEEE Trans Biomed Eng 59(9):2538–2548CrossRef
go back to reference Fraz MM, Rudnicka AR, Owen CG, Barman SA (2014) Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification. Int J Comput Assist Radiol Surg 9(5):795–811CrossRef Fraz MM, Rudnicka AR, Owen CG, Barman SA (2014) Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification. Int J Comput Assist Radiol Surg 9(5):795–811CrossRef
go back to reference GeethaRamani R, Balasubramanian L (2016) Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis. Biocybernetics and Biomedical Engineering 36(1):102–118CrossRef GeethaRamani R, Balasubramanian L (2016) Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis. Biocybernetics and Biomedical Engineering 36(1):102–118CrossRef
go back to reference Gou D, Wei Y, Fu H, Yan N (2018) Retinal vessel extraction using dynamic multiscale matched filtering and dynamic threshold processing based on histogram fitting. Mach vis Appl 29(4):655–666CrossRef Gou D, Wei Y, Fu H, Yan N (2018) Retinal vessel extraction using dynamic multiscale matched filtering and dynamic threshold processing based on histogram fitting. Mach vis Appl 29(4):655–666CrossRef
go back to reference Hoover A, Kouznetsova V, Goldbaum M (2000) Locating blood vessels in retinal images by piece-wise threshold probing of a matched filter response. IEEE Trans Med Imaging 19:203–210CrossRef Hoover A, Kouznetsova V, Goldbaum M (2000) Locating blood vessels in retinal images by piece-wise threshold probing of a matched filter response. IEEE Trans Med Imaging 19:203–210CrossRef
go back to reference Khan KB, Khaliq AA, Jalil A et al (2019) A review of retinal blood vessels extraction techniques: challenges, taxonomy, and future trends. Pattern Anal Appl 22(3):767–802MathSciNetCrossRef Khan KB, Khaliq AA, Jalil A et al (2019) A review of retinal blood vessels extraction techniques: challenges, taxonomy, and future trends. Pattern Anal Appl 22(3):767–802MathSciNetCrossRef
go back to reference Kolar R, Kubena T, Cernosek P, Budai A, Hornegger J, Gazarek J, Svoboda O, Jan J, Angelopoulou E, Odstrcilik J (2013) Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database. IE Image Process. 7(4):373–383MathSciNetCrossRef Kolar R, Kubena T, Cernosek P, Budai A, Hornegger J, Gazarek J, Svoboda O, Jan J, Angelopoulou E, Odstrcilik J (2013) Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database. IE Image Process. 7(4):373–383MathSciNetCrossRef
go back to reference Li Y, Sharan L, Adelson EH (2005) Compressing and companding high dynamic range images with subband architectures. A.C.M. Trans Graph (Proc. SIGGRAPH) 24(3) Li Y, Sharan L, Adelson EH (2005) Compressing and companding high dynamic range images with subband architectures. A.C.M. Trans Graph (Proc. SIGGRAPH) 24(3)
go back to reference Lupascu CA, Tegolo D, Trucco E (2010) FABC: retinal vessel segmentation using AdaBoost. IEEE Trans Inf Technol Biomed 14(5):1267–1274CrossRef Lupascu CA, Tegolo D, Trucco E (2010) FABC: retinal vessel segmentation using AdaBoost. IEEE Trans Inf Technol Biomed 14(5):1267–1274CrossRef
go back to reference Memari N, Ramli AR, Saripan MIB, Mashohor S, Moghbel M (2017) Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier. PLoS ONE 12(12):e188393CrossRef Memari N, Ramli AR, Saripan MIB, Mashohor S, Moghbel M (2017) Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier. PLoS ONE 12(12):e188393CrossRef
go back to reference Mo J, Zhang L (2017) Multi-level deep supervised networks for retinal vessel segmentation. Int J Comput Assist Radiol Surg 12(12):2181–2193CrossRef Mo J, Zhang L (2017) Multi-level deep supervised networks for retinal vessel segmentation. Int J Comput Assist Radiol Surg 12(12):2181–2193CrossRef
go back to reference Mondal R, Chatterjee RK, Kar A (2017) Segmentation of Retinal Blood Vessels Using Adaptive Noise Island Detection. In: Fourth International Conference on Image Information Processing (ICIIP) Mondal R, Chatterjee RK, Kar A (2017) Segmentation of Retinal Blood Vessels Using Adaptive Noise Island Detection. In: Fourth International Conference on Image Information Processing (ICIIP)
go back to reference Nandy RS, Chatterjee RK, Das A (2021) Segmentation of retinal blood vessel structure based on statistical distribution of the area of isolated objects. Recent Trends in Computational Intelligence Enabled Research. Academic Press, New York, pp 263–278CrossRef Nandy RS, Chatterjee RK, Das A (2021) Segmentation of retinal blood vessel structure based on statistical distribution of the area of isolated objects. Recent Trends in Computational Intelligence Enabled Research. Academic Press, New York, pp 263–278CrossRef
go back to reference Nandy RS, Chatterjee RK, Das A (2020) Segmentation of Blood Vessels from Fundus Image Using Scaled Grid. Machine Learning, Image Processing, Network Security and Data Sciences. MIND. Communications in Computer and Information Science. vol 1240. Springer, Singapore. https://doi.org/10.1007/978-981-15-6315-7_18 Nandy RS, Chatterjee RK, Das A (2020) Segmentation of Blood Vessels from Fundus Image Using Scaled Grid. Machine Learning, Image Processing, Network Security and Data Sciences. MIND. Communications in Computer and Information Science. vol 1240. Springer, Singapore. https://​doi.​org/​10.​1007/​978-981-15-6315-7_​18
go back to reference Niemeijer MJJ, Staal J, van Ginneken B et al (2004) Comparative study on retinal vessel segmentation methods on a new publicly available database. SPIE 2004:648–656 Niemeijer MJJ, Staal J, van Ginneken B et al (2004) Comparative study on retinal vessel segmentation methods on a new publicly available database. SPIE 2004:648–656
go back to reference Odstrcilik J, Kolar R, Budai A, Hornegger J, Jan J, Gazarek J, Kubena T, Cernosek P, Svoboda O, Angelopoulou E (2013) Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database. IET Image Process 7:373–383MathSciNetCrossRef Odstrcilik J, Kolar R, Budai A, Hornegger J, Jan J, Gazarek J, Kubena T, Cernosek P, Svoboda O, Angelopoulou E (2013) Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database. IET Image Process 7:373–383MathSciNetCrossRef
go back to reference Oliveira WS, Teixeira JV, Ren TI, Cavalcanti GD, Sijbers J (2016) Unsupervised retinal vessel segmentation using combined filters. PLoS ONE 11(2):e149943CrossRef Oliveira WS, Teixeira JV, Ren TI, Cavalcanti GD, Sijbers J (2016) Unsupervised retinal vessel segmentation using combined filters. PLoS ONE 11(2):e149943CrossRef
go back to reference Orlando JI, Prokofyeva E, Blaschko MB (2016) A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE Trans Biomed Eng 64(1):16–27CrossRef Orlando JI, Prokofyeva E, Blaschko MB (2016) A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE Trans Biomed Eng 64(1):16–27CrossRef
go back to reference Orlando JI, Blaschko M (2014) Learning fully-connected C.R.F.s for blood vessel segmentation in retinal images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp 634–641 Orlando JI, Blaschko M (2014) Learning fully-connected C.R.F.s for blood vessel segmentation in retinal images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp 634–641
go back to reference Otsu N (1979) A threshold selection method from gray-level histogram. IEEE Trans Syst Man Cybern 9(1):62–66CrossRef Otsu N (1979) A threshold selection method from gray-level histogram. IEEE Trans Syst Man Cybern 9(1):62–66CrossRef
go back to reference Owen CG, Rudnicka AR, Mullen R, Barman SA, Monekosso D, Whincup PH, Ng J, Paterson C (2009) Measuring retinal vessel tortuosity in 10-year-oldchildren: validation of the computer-assisted image analysis of the retina(Caiar) program. Invest Ophthalmol vis Sci 50:2004–2010CrossRef Owen CG, Rudnicka AR, Mullen R, Barman SA, Monekosso D, Whincup PH, Ng J, Paterson C (2009) Measuring retinal vessel tortuosity in 10-year-oldchildren: validation of the computer-assisted image analysis of the retina(Caiar) program. Invest Ophthalmol vis Sci 50:2004–2010CrossRef
go back to reference Paris S, Hasinoff SW, Kautz J (2015) Local Laplacian Filters: Edge-aware Image Processing with a Laplacian Pyramid. ACM Trans Graph. 58(3):81–91 Paris S, Hasinoff SW, Kautz J (2015) Local Laplacian Filters: Edge-aware Image Processing with a Laplacian Pyramid. ACM Trans Graph. 58(3):81–91
go back to reference Patton N et al (2006) Retinal image analysis: concepts, applications and potential. Prog Retin Eye Res 25:99–127CrossRef Patton N et al (2006) Retinal image analysis: concepts, applications and potential. Prog Retin Eye Res 25:99–127CrossRef
go back to reference Ricci E, Perfetti R (2007) Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans Med Imaging 26(10):1357–1365CrossRef Ricci E, Perfetti R (2007) Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans Med Imaging 26(10):1357–1365CrossRef
go back to reference Roychowdhury S, Koozekanani DD, Parhi KK (2014) Blood vessel segmentation of fundus images by major vessel extraction and subimage classification. IEEE J Biomed Health Inform 19(3):1118–1128 Roychowdhury S, Koozekanani DD, Parhi KK (2014) Blood vessel segmentation of fundus images by major vessel extraction and subimage classification. IEEE J Biomed Health Inform 19(3):1118–1128
go back to reference Roychowdhury S, Koozekanani DD, Parhi KK (2015) Iterative Vessel Segmentation of Fundus Images. IEEE Trans Biomed Eng 62(7):1738–1749CrossRef Roychowdhury S, Koozekanani DD, Parhi KK (2015) Iterative Vessel Segmentation of Fundus Images. IEEE Trans Biomed Eng 62(7):1738–1749CrossRef
go back to reference Sathananthavathi V, Indumathi G (2021) Encoder enhanced atrous (EEA) unet architecture for retinal blood vessel segmentation. Cognit Syst Res 67:84–95CrossRef Sathananthavathi V, Indumathi G (2021) Encoder enhanced atrous (EEA) unet architecture for retinal blood vessel segmentation. Cognit Syst Res 67:84–95CrossRef
go back to reference Staal J, Abramoff 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):501–509CrossRef Staal J, Abramoff 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):501–509CrossRef
go back to reference Tang S, Lin T, Yang J, Fan J, Ai D, Wang Y (2015) Retinal vessel segmentation using supervised classification based on multiscale vessel filtering and Gabor wavelet. J Med Imag Health Inform 5(7):1571–1574CrossRef Tang S, Lin T, Yang J, Fan J, Ai D, Wang Y (2015) Retinal vessel segmentation using supervised classification based on multiscale vessel filtering and Gabor wavelet. J Med Imag Health Inform 5(7):1571–1574CrossRef
go back to reference Thangaraj S, Periyasamy V, Balaji R (2017) Retinal vessel segmentation using neural network. IET Image Process 12(5):669–678CrossRef Thangaraj S, Periyasamy V, Balaji R (2017) Retinal vessel segmentation using neural network. IET Image Process 12(5):669–678CrossRef
go back to reference Vega R, Sanchez-Ante G, Falcon-Morales LE, Sossa H, Guevara E (2015) Retinal vessel extraction using Lattice Neural Networks with dendritic processing. Comput Biol Med 58:20–30CrossRef Vega R, Sanchez-Ante G, Falcon-Morales LE, Sossa H, Guevara E (2015) Retinal vessel extraction using Lattice Neural Networks with dendritic processing. Comput Biol Med 58:20–30CrossRef
go back to reference Wu H, Wang W, Zhong J, Lei B, Wen Z, Qin J (2021) SCS-Net: Ascale and context sensitive network for retinal vessel segmentation. Med Image Anal 70:102025CrossRef Wu H, Wang W, Zhong J, Lei B, Wen Z, Qin J (2021) SCS-Net: Ascale and context sensitive network for retinal vessel segmentation. Med Image Anal 70:102025CrossRef
go back to reference Yan Z, Yang X, Cheng KTT (2019) A three-stage deep learning model for accurate retinal vessel segmentation. IEEE J Biomed Health Inform 23(4):1427–1436CrossRef Yan Z, Yang X, Cheng KTT (2019) A three-stage deep learning model for accurate retinal vessel segmentation. IEEE J Biomed Health Inform 23(4):1427–1436CrossRef
go back to reference Yue K, Zou B, Chen Z, Liu Q (2018) Improved multiscale line detection method for retinal blood vessel segmentation. IET Image Process 12(8):1450–1457CrossRef Yue K, Zou B, Chen Z, Liu Q (2018) Improved multiscale line detection method for retinal blood vessel segmentation. IET Image Process 12(8):1450–1457CrossRef
go back to reference Zana F, Klein JC (2001) Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans Image Process 10(7):1010–1019CrossRef Zana F, Klein JC (2001) Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans Image Process 10(7):1010–1019CrossRef
go back to reference Zhang B, Zhang L, Zhang L, Karray F (2010) Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput Biol Med 40(4):438–445CrossRef Zhang B, Zhang L, Zhang L, Karray F (2010) Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput Biol Med 40(4):438–445CrossRef
Metadata
Title
Fundus vessel structure segmentation based on Bel-Hat transformation
Authors
Rajat Suvra Nandy
Rohit Kamal Chatterjee
Abhishek Das
Publication date
24-11-2023
Publisher
Springer Berlin Heidelberg
Published in
Microsystem Technologies / Issue 4/2024
Print ISSN: 0946-7076
Electronic ISSN: 1432-1858
DOI
https://doi.org/10.1007/s00542-023-05552-4