SpringerLink
Log in

A comprehensive study of optic disc detection in artefact retinal images using a deep regression neural network for a fused distance-intensity map

  • Original Article
  • Published:
Research on Biomedical Engineering Aims and scope Submit manuscript

Abstract

Purpose

Optic disc (OD, hereafter) detection is often the first step to detect other retinal landmarks for analysis of conditions such as glaucoma and diabetic retinopathy. It is often not possible to localize the OD based on colour/pixel information alone, especially for poor-contrast and low-resolution images. Community camp-based images under poor lighting conditions and hand-held ophthalmoscopes also induces imaging artefacts.

Methods

The paper proposes an automatic OD detection method using a U-Net-based regression, with a distance-intensity map. The regression network uses Tukey’s biweight loss function to make it robust to outliers, and improve the overall rate of convergence. The method localizes OD coordinates using a Generalized Laplacian-of-Gaussian (gLoG) operator on the predicted distance-intensity map. The system shows encouraging experimental results on poor resolution images with non-uniform illumination, noise, motion, blurring, and various imaging artefacts.

Results

The experiments with intra- and inter-dataset performance (training and testing on different datasets) are with the following datasets: Messidor, Kaggle, DRIVE, DRIONS, STARE, and Drishti-GS. The method shows excellent qualitative and quantitative results on Messidor and the challenging AIIMS Community Camp dataset, as well. The achieved detection accuracy is of 99.67% and 98.83%, respectively.

Conclusion

The U-Net-based regressor with the novel loss function is geared towards getting good optic disc detection performance across a large number of datasets. The network shows robust detection performance on challenging images with various retinal artifacts (blurring, poor illumination, and clinical pathologies).

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
EUR 32.99 /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
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Al-Bander B, Al-Nuaimy W, Williams BM, Zheng Y. Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc. Biomed Signal Process Control. 2018;40:91–101.

    Article  Google Scholar 

  • Avinash Ramakanth S, Venkatesh Babu R. Approximate nearest neighbour nield based optic disk detection. Comput Med Imaging Graph. 2014;38(1):49–56.

    Article  Google Scholar 

  • Belagiannis V, Rupprecht C, Carneiro G, Navab N. Robust optimization for deep regression. In: Proc. IEEE International Conference on Computer Vision (ICCV), 2015. p. 2830–2838.

  • Calimeri F, Marzullo A, Stamile C, Terracina G. Optic disc detection using fine tuned convolutional neural networks. In: Proc. International conference on signal-image technology & internet-based systems, 2016. p. 69–75.

  • Czarnowski I, Jedrzejowicz P. An approach to data reduction for learning from big datasets: integrating stacking, rotation, and agent population learning techniques. Complexity 2018:1–13.

  • Dada T, Coote M. Clinical evaluation of optic nerve head. International Society of Glaucoma Surgery. 2010.

  • Dietter J, Haq W, Ivanov IV, Norrenberg LA, Völker M, Dynowski M, Röck D, Ziemssen F, Leitritz MA, Ueffing M. Optic disc detection in the presence of strong technical artifacts. Biomed Signal Process Control. 2019;53:1–11.

    Article  Google Scholar 

  • Foracchia M, Grisan E, Ruggeri A. Detection of optic disc in retinal images by means of a geometrical model of vessel structure. IEEE Trans Med Imaging. 2004;23:1189–95.

    Article  Google Scholar 

  • Fu Y, Chen J, Li J, Pan D, Yue X, Zhu Y. Optic disc segmentation by u-net and probability bubble in abnormal fundus images. Pattern Recognit. 2021;117: 107971.

    Article  Google Scholar 

  • Huang Y, Zhong Z, Yuan J, Tang X. Efficient and robust optic disc detection and fovea localization using region proposal network and cascaded network. Biomed Signal Process Control. 2020;60:1–10.

    Article  Google Scholar 

  • Isola P, Zhu JY, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. ar**v:1611.07004.

  • Jana S, Parekh R, Sarkar B. A semi-supervised approach for automatic detection and segmentation of optic disc from retinal fundus image. In: Nayak J, Naik B, Pelusi D, Das AK, editors. Handbook of computational intelligence in biomedical engineering and healthcare. Academic Press; p. 65–91.

  • Kaggle diabetic retinopathy detection competition. 2015;https://www.kaggle.com/c/diabetic-retinopathy-detection/data, Accessed: 20 July, 2015.

  • Karras T, Laine S, Aila T. A style-based generator architecture for generative adversarial networks. ar**v:1812.04948.

  • Kong H, Akakin HC, Sarma SE. A generalized laplacian of gaussian filter for blob detection and its applications. IEEE Trans Cybern. 2013;43(6):1719–33.

    Article  Google Scholar 

  • Lalonde M, Beaulieu M, Gagnon L. Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching. IEEE Trans Med Imaging. 2001;20(11):1193–200.

    Article  Google Scholar 

  • Latif J, Tu S, **ao C, Rehman SU, Imran A, Latif Y. Odgnet: a deep learning model for automated optic disc localization and glaucoma classification using fundus images. SN Appl Sci. 2022;4:98.

    Article  Google Scholar 

  • Liang M, Zhang Y, Wang H, Li J. Location of optic disk in the fundus image based on visual attention. In: Proc. International Conference on Computer Information and Big Data Applications (CIBDA). p. 446–449.

  • Lu S. Accurate and efficient optic disc detection and segmentation by a circular transformation. IEEE Trans Med Imaging. 2011;30:2126–33.

  • Maninis KK, Pont-Tuset J. Retinal databases. http://www.vision.ee.ethz.ch/~cvlsegmentation/driu/downloads.html.

  • Mendonça AM, Sousa A, Mendonça L, Campilho A. Automatic localization of the optic disc by combining vascular and intensity information. Comput Med Imaging Graph. 2013;37(5–6):409–17.

    Article  Google Scholar 

  • Messidor Retinal Database. 2016.http://www.adcis.net/en/third-party/messidor/, Accessed: 24 September, 2016.

  • Meyer MI, Galdran A, Medonça AM, Campilho A. A pixel-wise distance regression approach for joint retinal optical disc and fovea detection. In: Proc. Medical Image Computing and Computer-Assisted Intervention (MICCAI). p. 39–47.

  • Mitra A, Banerjee PS, Roy S, Roy S, Setua SK. The region of interest localization for glaucoma analysis from retinal fundus image using deep learning. Comput Methods Programs Biomed. 2018;165:25–35.

    Article  Google Scholar 

  • Nawaz M, Nazir T, Javed A, Tariq U, Yong HS, Khan MA, Cha J. An efficient deep learning approach to automatic glaucoma detection using optic disc and optic cup localization. Sensors. 2022;22(2):434.

    Article  Google Scholar 

  • Nergiz M, Akin M, Yildiz A, Ömer Takeç. Automated fuzzy optic disc detection algorithm using branching of vessels and color properties in fundus images. Biocybern Biomed Eng. 2018;38(4):850–867.

  • Niemeijer M, Abràmoff MD, van Ginneken B. Fast detection of the optic disc and fovea in color fundus photographs. Med Image Anal. 2009;13(6):859–70.

    Article  Google Scholar 

  • Niu D, Xu P, Wan C, Cheng J, Liu J. Automatic localization of optic disc based on deep learning in fundus images. In: Proc. International Conference on Signal and Image Processing. 2017 p. 208–212.

  • Panda R, Puhan NB, Panda G. Robust and accurate optic disk localization using vessel symmetry line measure in fundus images. Biocybern Biomed Eng. 2017;37(3):466–76.

    Article  Google Scholar 

  • Pathan S, Kumar P, Pai R, Bhandary S. Automated detection of optic disc contours in fundus images using decision tree classifier. Biocybern Biomed Eng. 2019;40:1–13.

    Google Scholar 

  • Pathan S, Kumar P, Pai RM, Bhandary SV. Automated segmentation and classifcation of retinal features for glaucoma diagnosis. Biomed Signal Process Control. 2021;63:1–19.

    Article  Google Scholar 

  • Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Proc. Medical Image Computing and Computer-Assisted Intervention (MICCAI), vol 9351. 2015, p. 234–241.

  • Serte S, Serener A. Graph-based Saliency and ensembles of convolutional neural networks for glaucoma detection. IET Image Process. 2021;15:1–8.

  • Sharma A, Agrawal M, Lall B. Optic disc detection using vessel characteristics and disc features. In: Proc. National Conference on Communications (NCC). 2017, p. 1–6.

  • Shukla AK, Pandey RK, Pachori RB. A fractional filter based efficient algorithm for retinal blood vessel segmentation. Biomed Signal Process Control. 2020;59:1–16.

    Article  Google Scholar 

  • Sivaswamy J, Krishnadas SR, Joshi GD, Jain M, Ujjwal, Syed-Tabish A. Drishti-GS: retinal image dataset for optic nerve head (ONH) segmentation. In: Proc. IEEE International Symposium on Biomedical Imaging (ISBI). 2014

  • Soares I, Castelo-Branco M, Pinheiro AM. Optic disc localization in retinal images based on cumulative sum fields. IEEE J Biomed Health Inform. 2016;20:574–85.

    Article  Google Scholar 

  • Subha V, Rayen S. A novel visual saliency thresholding method for accurate ROI segmentation in optic disc from FEIs for glaucoma detection. Int J Comput Digital Syst. 2022;11:477–89.

    Article  Google Scholar 

  • Trucco E, MacGillivray T, Xu Y. Computational retinal image analysis: tools, applications and perspectives. Mara Conner. 2019.

  • Tulsani A, Kumar P, Pathan S. Automated segmentation of optic disc and optic cup for glaucoma assessment using improved unet++ architecture. Biocybern Biomed Eng. 2021;41(2):819–32.

    Article  Google Scholar 

  • Wankhede PR, Khanchandani KB. Optic disc detection using template matching based on color plane histograms. In: Proc. International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC). p. 278–281.

  • Wei Z, Hao W, Chengdong W, **aosheng Y, Yugen Y. Automatic optic disc detection in color retinal images by local feature spectrum analysis. 2018.

  • **ong L, Li H. An approach to locate optic disc in retinal images with pathological changes. Comput Med Imaging Graph. 2016;47:40–50.

    Article  Google Scholar 

  • Yu H, Barriga ES, Agurto C, Echegaray S, Pattichis MS, Bauman W, Soliz P. Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level sets. IEEE Trans Inf Technol Biomed. 2012;16(4):644–57.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Technology Delhi, New Delhi, India

    Ambika Sharma, Monika Agrawal & Sumantra Dutta Roy

  2. All India Institute of Medical Sciences, New Delhi, India

    Vivek Gupta

Authors
  1. Ambika Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Monika Agrawal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sumantra Dutta Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vivek Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ambika Sharma.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) 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

Sharma, A., Agrawal, M., Dutta Roy, S. et al. A comprehensive study of optic disc detection in artefact retinal images using a deep regression neural network for a fused distance-intensity map. Res. Biomed. Eng. 39, 639–653 (2023). https://doi.org/10.1007/s42600-023-00294-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42600-023-00294-8

Keywords

Search

Navigation