Skip to main content

Advertisement

SpringerLink
Log in

Towards developing an automated technique for glaucomatous image classification and diagnosis (AT-GICD) using neural networks

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Glaucoma is the eye defect that has become the second leading cause of blindness worldwide and also stated as incurable, may cause complete vision loss. The earlier diagnosis of glaucoma in Human Eye is a great confrontation and very important in present scenario, for providing efficient and appropriate treatments to the persons. Though there is much advancement in Ocular Imaging that affords methods for earlier detection, the appropriate results can be obtained by integrating the data from structural and functional evaluations. With that note, this paper involves in developing automated technique for glaucomatous image classification and diagnosis (AT-GICD). The model considers both the textural and energy features for effectively diagnosing the defect. Image Segmentation is processed for obtaining the exact area of optic nerve head; histogram gradient based conversion is employed for enhancing the fundus image features. Further, Wavelet Energy features are extracted and applied to the artificial neural networks (ANN) for classifying the NORMAL and GLAUCOMA images. The Accuracy rate based comparison with other existing models is carried out for evidencing the effectiveness of the proposed model in glaucomatous image classification.

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
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Quigley HA (1996) Number of people with glaucoma worldwide. Br J Ophthalmol 80(5):389–393

    Article  Google Scholar 

  2. Varma R, Peeples P, Walt JG, Bramley TJ (2008) Disease progression and the need for neuroprotection in glaucoma management. Am J Manag Care 14:15–19

    Google Scholar 

  3. Quigley HA, Broman AT (2006) The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol 90(3):262–267

    Article  Google Scholar 

  4. Gamero GE, Robert DF (2007) The optic nerve in glaucoma. In: Choplin NT, Lundy D (eds) Atlas of glaucoma, 2nd edn. CRC Press, Boca Raton

    Google Scholar 

  5. Anindita Septiarini A, Harjoko A (2015) Automatic glaucoma detection based on the type of features used: a review. J Theor Appl Inf Technol 72(3):366–375

    Google Scholar 

  6. Salam AA, Khalil T, Akram MU, Jameel A, Basit I (2016) Automated detection of glaucoma using structural and non-structural features. SpringerPlus Med 5(1):1–21

    Google Scholar 

  7. Issac A, Partha Sarathi M, Dutta MK (2015) An adaptive threshold based image processing technique for improved glaucoma detection and classification. Comput Methods Prog Biomed 122(2):229–344

    Article  Google Scholar 

  8. Marin D, Gegundez-Arias ME, Suero A, Bravo JM (2015) Obtaining optic disc center and pixel region by automatic thresholding methods on morphologically processed fundus images. Comput Methods Progr Biomed 118(2):173–185

    Article  Google Scholar 

  9. Septiarini A, Harjoko A, Pulungan R, Ekantini R (2017) Optic disc and cup segmentation by automatic thresholding with morphological operation for glaucoma evaluation. Signal Image Video Process 11(5):945–952

    Article  Google Scholar 

  10. Muramatsu C, Nakagawa T, Sawada A, Hatanaka Y, Hara T, Yamamoto T (2011) Automated segmentation of optic disc region on retinal fundus photographs: comparison of contour modelling and pixel classification methods. Comput Methods Programs Biomed 101(1):23–32

    Article  Google Scholar 

  11. Mary MCVS, Rajsingh EB, Jacob JKK, Anandhi D, Amato U, Easter Selvan S (2015) An empirical study on optic disc segmentation using an active contour model. Biomed Signal Process Control 18:19–29

    Article  Google Scholar 

  12. Cheng J, Liu J, Xu Y, Yin F, Wong DWK, Tan N-M, Tao D (2013) Super pixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans Med Imaging 32(6):1019–1032

    Article  Google Scholar 

  13. Nicolela MT, Coleman AL, Caprioli J (2010) Optic nerve: clinical examination. In: Giaconi JA, Law SK (eds) Pearls of glaucoma management. Springer, Berlin, pp 15–21

    Chapter  Google Scholar 

  14. Garway-Heath DF, Ruben S, Viswanathan A, Hitchings RA (1998) Vertical cup/disc ratio in relation to optic disc size: its value in the assessment of the glaucoma suspect. Brit J Ophthalmol 82(10):1118–1124

    Article  Google Scholar 

  15. Panda R, Puhan NB, Rao A, Padhy D, Panda G (2017) Recurrent neural network based retinal nerve fiber layer defect detection in early glaucoma. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, 2017, pp. 692–695

  16. Şahi̇n ME, Bozkurt MR (2015) Retinal nerve fiber layer detection at optical coherence tomography image for the diagnosis of glaucoma. In: 2015 Medical Technologies National Conference, Bodrum, pp 1–3

  17. Kavya N, Padmaja KV (2017) Glaucoma detection using texture features extraction. In: 2017 51st Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, pp 1471–1475

  18. Patil DD, Manza RR, Bedke GC, Rathod DD (2015) Development of primary glaucoma classification technique using optic cup & disc ratio. In: 2015 International Conference on Pervasive Computing (ICPC), Pune, pp 1–12

  19. Roslin M, Sumathi S (2016) Glaucoma screening by the detection of blood vessels and optic cup to disc ratio. In: 2016 International Conference on Comm. and Signal Processing, Melmaruvathur, pp 2210–2215

  20. Nikam SM, Patil (2017) Glaucoma detection from fundus images using MATLAB GUI. In: 2017 3rd International Conference on Advances in Comp. Comm. & Automation (ICACCA) (Fall), Dehradun, pp 1–4

  21. Jose AM, Balakrishnan AA (2015) A novel method for glaucoma detection using optic disc and cup segmentation in digital retinal fundus images. In: 2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015], Nagercoil, pp 1–5

  22. Ahmad H, Yamin A, Shakeel A, Gillani SO, Ansari (2014) Detection of glaucoma using retinal fundus images. In: 2014 International Conference on Robotics and Emerging Allied Technologies in Engineering (iCREATE), Islamabad, pp 321–324

  23. Pinos-Velez E, Flores-Rivera M, Ipanque-Alama W, Herrera-Alvarez D, Luis C (2018) Implementation of support tools for the presumptive diagnosis of Glaucoma through identification and processing of medical images of the human eye. In: 2018 IEEE International Systems Engineering Symposium (ISSE), Rome, 2018, pp 1–5

  24. Atheesan S, Yashothara S (2016) Automatic glaucoma detection by using funduscopic images. In: 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, pp 813–817

  25. Virk JK, Singh M, Singh M (2015) Cup-to-disk ratio (CDR) determination for glaucoma screening. In: 2015 1st International Conference on Next Generation Computing Technologies, Dehradun, pp 504–507

  26. Kusumandari D, Munandar A, Redhyka GG (2015) The comparison of GVF snake active contour method and ellipse fit in optic disc detection for glaucoma diagnosis. In: 2015 International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT), Bandung, pp 123–126

  27. Deepika E, Maheswari S (2018) Earlier glaucoma detection using blood vessel segmentation and classification. In: 2018 2nd International Conference on Inventive Systems and Control (ICISC), Coimbatore, pp 484–490

  28. Mookiah MRK, Acharya UR, Lim CM, Petznick A, Sur JS (2012) Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features. Knowl Based Syst 33:73–82

    Article  Google Scholar 

  29. Annu N, Justin J (2013) Classification of glaucoma images using wavelet based energy features and PCA. Int J Sci Eng Res 4(5):1369–1374

    Google Scholar 

  30. Dey A, Bandyopadhyay SK (2016) Automated glaucoma detection using support vector machine classification method. Br J Med Med Res 11(12):1–12

    Article  Google Scholar 

  31. Singh A, Dutta MK, Partha-Sarathi M, Uher V, Burget R (2016) Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image. Comput Methods Programs Biomed 124:108–120

    Article  Google Scholar 

  32. Karthikeyan S, Rengarajan N (2014) Performance analysis of gray level co-occurrence matrix texture features for glaucoma diagnosis. Am J Appl Sci 11(2):248–257

    Article  Google Scholar 

  33. Ali MA, Hurtut T, Faucon T, Cheriet F (2014) Glaucoma detection based on local binary patterns in fundus photographs. In: Proceedings of SPIE Medical Imaging 2014: Image Processing; 2014 Feb 16–18; San Diego, CA

  34. Aruchamy S, Bhattacharjee P, Sanyal G (2015) Automated glaucoma screening in retinal fundus images. Int J Multimed Ubiquitous Eng 10(9):129–136

    Article  Google Scholar 

  35. Murthi A, Madheswaran M (2014) Medical decision support system to identify glaucoma using cup to disc ratio. J Theor Appl Inf Technol 68(2):406–413

    Google Scholar 

  36. Septiarini A, Hamdani H, Khairina DM (2016) The contour extraction of cup in fundus images for glaucoma detection. Int J Electr Comput Eng 6(6):2797–2804

    Google Scholar 

  37. Pal A, Moorthy MR, Shahina A (2018) G-Eyenet: a convolutional auto encoding classifier framework for the detection of glaucoma from retinal fundus images. In: 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, pp 2775–2779

  38. Baidaa AB, Bryan MW, Waleed AN, Majid AALT, Pand H, Yalin Z (2018) Dense fully convolutional segmentation of the optic disc and cup in colour fundus for glaucoma diagnosis. MDPI J Symm 10(4):1–16

    Google Scholar 

  39. Sivaswamy J, Krishnadas SR, Datt-Joshi G, Jain M, Syed-Tabish AU (2014) Drishti-GS: retinal image dataset for optic nerve head (ONH) segmentation. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), Beijing, pp 53–56

  40. Sarsam SM, Al-Samarraie H (2022) Early-stage detection of eye diseases on microblogs: glaucoma recognition. Int J Inf Technol 14:255–264

    Google Scholar 

  41. Rajyaguru V, Vithalani C, Thanki R (2022) A literature review: various learning techniques and its applications for eye disease identification using retinal images. Int J Inf Technol 14:713–724

    Google Scholar 

  42. Patil P, Jagtap S (2020) Multi-modal biometric system using finger knuckle image and retina image with template security using PolyU and DRIVE database. Int J Inf Technol 12:1043–1050

    Google Scholar 

  43. Patil S, Patil KR, Patil CR et al (2020) Performance overview of an artificial intelligence in bio medics: a systematic approach. Int J Inf Technol 12:963–973

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of CSE, R.M.K Engineering College, Chennai, India

    M. P. Karthikeyan

  2. Department of CSE, CHRIST (Deemed to be University), Bengaluru, India

    E. A. Mary Anita

  3. Department of ECE, Sri Krishna College of Engineering and Technology, Kuniyamuthur, India

    D. Mohana Geetha

Authors
  1. M. P. Karthikeyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. A. Mary Anita
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. D. Mohana Geetha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. P. Karthikeyan.

Ethics declarations

Conflict of interest

The authors whose names are listed immediately below certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

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

Karthikeyan, M.P., Mary Anita, E.A. & Mohana Geetha, D. Towards developing an automated technique for glaucomatous image classification and diagnosis (AT-GICD) using neural networks. Int. j. inf. tecnol. 15, 3727–3739 (2023). https://doi.org/10.1007/s41870-023-01313-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-023-01313-8

Keywords

Search

Navigation