Skip to main content
SpringerLink
Log in

Retinal vessels segmentation of colour fundus images using two stages cascades convolutional neural networks

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Retinal vascular status in fundus images is a reliable biomarker for diabetes, hypertension and many ophthalmic diseases. Therefore, accurate segmentation of retinal vessels is of great significance for the diagnosis of many diseases. However, due to the inherent complexity of the retina itself and the lack of data, it is difficult to obtain the ideal accuracy of the segmentation results of the vascular end. To solve this problem, we propose an innovative two stage cascades convolutional Neural network (TCCNNet) to segment the retinal vessels in fundus images. In stage 1, an entire down sampled fundus images is sent to a probability-anatomical-prior-guided shallow-layer-enhanced location net to perform rough segmentation. In addition, a novel circular inference module and parameter Dice loss are proposed to reduce the uncertain probabilities and false positive points of the boundary. In stage 2, high-resolution 2D image slices are reconstructed to offset the detailed information lost during downsampling, further refining the contours. Moreover, a multi-view 2.5D net composed of three 2D refinement subnetworks is applied to deeply explore the morphological features, compensating for the mistakes and missing spatial information of a single view. The proposed method is evaluated on different evaluation metrics such as sensitivity, specificity and accuracy. The experimental results show that this method shows the most competitive performance compared to other state of the art methods.

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

Similar content being viewed by others

References

  • Arokia Jesu Prabhu L, Jayachandran A (2018) Mixture model segmentation system for parasagittal meningioma brain tumor classification based on hybrid feature vector. J Med Syst 42(12):1–6

    Article  Google Scholar 

  • Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39:2481–2495

    Article  Google Scholar 

  • Chang X, Shen H, Nie F, Wang S, Yang Y, Zhou X (2016) Compound rank k-projections for bilinear analysis. IEEE Trans Neural Netw Learn Syst 27(7):1502–1513

    Article  MathSciNet  Google Scholar 

  • Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder- decoder with atrous separable convolution for semantic image segmentation. In: Proc. Eur. Conf. Comput. Vision, pp 833–851

  • Feng S, Zhuo Z, Pan D, Tian Q (2020) CcNet: a cross-connected convolutional network for segmenting retinal vessels using multi-scale features. Neurocomputing 392:268–276

    Article  Google Scholar 

  • Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit, pp. 3141–3149

  • Ghiasi G, Lin T-Y, Le QV (2018) DropBlock: a regularization method for convolutional networks. https://arXiv.org/1810.12890

  • Hu J, Shen L, Albanie S, Sun G, Wu E (2020) Squeeze-and-Excitation Networks. IEEE Trans Pattern Anal Mach Intell 42:2011–2023

    Article  Google Scholar 

  • Jayachandran A, David S (2020) A new expert system based on hybrid colour and structure descriptor and machine learning algorithms for early glaucoma diagnosis. Multimedia Tools Appl 79(7):5213–5224

    Google Scholar 

  • Jayachandran A, Dhanasekaran R (2013) Automatic detection of brain tumor in magnetic resonance images using multi-texton histogram and support vector machine. Int J Imaging Syst Technol 23(2):97–103

    Article  Google Scholar 

  • Jayachandran A, Dhanasekaran R (2017) Multi class brain tumor classification of MRI images using hybrid structure descriptor and fuzzy logic based RBF kernel SVM. Iran J Fuzzy Syst 14(3):41–54

    MathSciNet  Google Scholar 

  • Jayachandran A, Kharmega Sundararaj G (2016) Abnormality segmentation and classification of multi model brain tumor in MR images using Fuzzy based hybrid kernel SVM. Int J Fuzzy Syst 17(3):434–443

    Article  Google Scholar 

  • Jin Q, Meng Z, Pham TD, Chen Q, Wei L, Su R (2019) DUNet: a deformable network for retinal vessel segmentation. Knowl-Based Syst 178:149–162

    Article  Google Scholar 

  • Lam B, Gao Y, Liew AW (2010) General retinal vessel segmentation using regularization-based multiconcavity modeling. IEEE Trans Med Imaging 29:1369–1381

    Article  Google Scholar 

  • Li Z, Nie F, Chang X, Yang Y, Zhang C (2018a) Rank-constrained spectral clustering with flexible embedding. IEEE Trans Neural Netw Learn Syst 29(12):6073–6082

    Article  MathSciNet  Google Scholar 

  • Li Z, Nie F, Chang X, Yang Y, Zhang C (2018b) Dynamic affinity graph construction for spectral clustering using multiple features. IEEE Trans Neural Netw Learn Syst 29(12):6323–6332

    Article  MathSciNet  Google Scholar 

  • Li Z, Yao L, Chang X, Zhang K, Sun J, Zhang H (2019) Zero-shot event detection via event-adaptive concept relevance mining. Pattern Recogn 88:595–603

    Article  Google Scholar 

  • Li X, Jiang Y, Li M, Yin S (2021) Lightweight attention convolutional neural network for retinal vessel image segmentation. IEEE Trans Ind Inform 17:1958–1967

    Article  Google Scholar 

  • Luo M, Chang X, Nie L, Yang Y, Hauptmann AG, Zheng Q (2018) An adaptive semisupervised feature analysis for video semantic recognition. IEEE Trans Cybern 48(2):648–660

    Article  Google Scholar 

  • Mahiba C, Jayachandran A (2019) Severity analysis of diabetic retinopathy in retinal images using hybrid structure descriptor and modified CNNs. Measurements 135:762–767

    Google Scholar 

  • Milletari F, Navab N, Ahmadi S (2016) V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: Proc.- Int. Conf. 3D Vis., pp 565–571

  • Odstrcilik R, Kolar A, Budai J, Hornegger J, Jan J, Gazarek T, Kubena P, Cernosek OS, Angelopoulou E (2013) Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database. IET Image Process 7(4):373–383

    Article  MathSciNet  Google Scholar 

  • Owen CG, Rudnicka AR, Mullen R, Barman SA, Monekosso D, Whincup PH, Ng J, Paterson C (2009) Measuring retinal vessel tortuosity in 10-year-old children: validation of the computer-assisted image analysis of the retina (CAIAR) program. Invest Ophthalmol vis Sci 50:2004–2010

    Article  Google Scholar 

  • Palanivel DA, Natarajan S, Gopalakrishnan S (2020) Retinal vessel segmentation using multifractal characterization. Appl Soft Comput 94:106439

    Article  Google Scholar 

  • Rezaee K, Haddadnia J, Tashk A (2017) Optimized clinical segmentation of retinal blood vessels by using combination of adaptive filtering, fuzzy entropy and skeletonization. Appl Soft Comput 52:937–951

    Article  Google Scholar 

  • Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Proc. Med. Image Comput. Comput. Assist. Intervention. pp 234–241

  • Samuel PM, Veeramalai T (2021) VSSC Net: vessel specific skip chain convolutional network for blood vessel segmentation. Comput Meth Programs Biomed 198:105769

    Article  Google Scholar 

  • Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651

    Article  Google Scholar 

  • 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 Med Imaging 25:1214–1222

    Article  Google Scholar 

  • Soomro TA, Afifi AJ, Gao J, Hellwich O, Zheng L, Paul M (2019) Strided fully convolutional neural network for boosting the sensitivity of retinal blood vessels segmentation. Expert Syst Appl 134:36–52

    Article  Google Scholar 

  • Sopharak A, Uyyanonvara B, Barman S (2013) Simple hybrid method for fine microaneurysm detection from non-dilated diabetic retinopathy retinal images. Comput Med Imaging Graph 37:394–402

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Tang X, Zhong B, Peng J, Hao B, Li J (2020a) Multi-scale channel importance sorting and spatial attention mechanism for retinal vessels segmentation. Appl Soft Comput 93:106353

    Article  Google Scholar 

  • Tang X, Zhong B, Peng J, Hao B, Li J (2020b) Multi-scale channel importance sorting and spatial attention mechanism for retinal vessels segmentation. Appl Soft Comput 93:106353

    Article  Google Scholar 

  • Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. https://arXiv.org/1706.03762

  • Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp 11531–11539

  • Wu Y, Xia Y, Song YZ, Cai W (2020) NFN+: a novel network followed network for retinal vessel segmentation. Neural Netw 126:153–162

    Article  Google Scholar 

  • Xiang, Gao X, Zou B, Zhu C, Qiu C, Li X (2014) Segmentation of retinal blood vessels based on divergence and bot-hat transform. In: Proc. IEEE Int. Conf. Prog. Informatics Comput, pp 316–320

  • Yan Z, Yang X, Cheng K-T (2018) Joint segment-level and pixel wise losses for deep learning based retinal vessel segmentation. IEEE Trans Biomed Eng 65:1912–1923

    Article  Google Scholar 

  • Yan Z, Yang X, Cheng K-T (2019) A three-stage deep learning model for accurate retinal vessel segmentation. IEEE J Biomed Health Inform 23:1427–1436

    Article  Google Scholar 

  • Yan C, Chang X, Luo M, Zheng Q, Zhang X, Li Z (2021) Self-weighted robust LDA for multiclass classification with edge classes. ACM Trans Intell Syst Technol 12(1):1–19

    Article  Google Scholar 

  • Zhang J, Dashtbozorg B, Bekkers E, Pluim JPW, Duits R, ter Haar Romeny BM (2016) Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores. IEEE Trans Med Imaging 35:2631–2644

    Article  Google Scholar 

  • Zhang D, Yao L, Chen K, Wang S (2020) Making sense of spatio-temporal preserving representations for EEG-based human intention recognition. IEEE Trans Cybern 50(7):3033–3044

    Article  Google Scholar 

  • Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2020a) UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging 39:1856–1867

    Article  Google Scholar 

  • Zhou R, Chang X, Shi L, Shen Y-D, Yang Y, Nie F (2020b) Person reidentification via multi-feature fusion with adaptive graph learning. IEEE Trans Neural Netw Learn Syst 31(5):1592–1601 

    Article  Google Scholar 

Download references

Funding

This work was not supported from any Funding Agencies.

Author information

Authors and Affiliations

  1. Department of CSE, Presidency University, Bangalore, India

    A. Jayachandran & G. Shunmugarathinam

  2. Department of CSE, Rohini College of Engineering and Technology, Nagercoil, India

    T. Sudarson Rama Perumal

Authors
  1. A. Jayachandran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. Shunmugarathinam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T. Sudarson Rama Perumal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Jayachandran.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Ethical approval statement

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jayachandran, A., Shunmugarathinam, G. & Perumal, T.S.R. Retinal vessels segmentation of colour fundus images using two stages cascades convolutional neural networks. J Ambient Intell Human Comput 14, 9305–9315 (2023). https://doi.org/10.1007/s12652-022-04429-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-022-04429-0

Keywords

Search

Navigation