Glaucoma Detection from Retinal Images Using Statistical and Textural Wavelet Features

  • Lamiaa Abdel-HamidEmail author


Glaucoma is a silent progressive eye disease that is among the leading causes of irreversible blindness. Early detection and proper treatment of glaucoma can limit severe vision impairments associated with advanced stages of the disease. Periodic automatic screening can help in the early detection of glaucoma while reducing the workload on expert ophthalmologists. In this work, a wavelet-based glaucoma detection algorithm is proposed for real-time screening systems. A combination of wavelet-based statistical and textural features computed from the detected optic disc region is used to determine whether a retinal image is healthy or glaucomatous. Two public datasets having different resolutions were considered in the performance analysis of the proposed algorithm. An accuracy of 96.7% and area under receiver operating curve (AUC) of 94.7% were achieved for the high-resolution dataset. Analysis of the wavelet-based statistical and textural features using three different methods showed their relevance for glaucoma detection. Furthermore, the proposed algorithm is shown to be suitable for real-time applications as it requires less than 3 s for processing the high-resolution retinal images.


Glaucoma Retinal images Wavelet transform Gray-level co-occurrence matrix Statistical features Classification 


Compliance with Ethical Standards

Conflict of Interest

The author declares that there is no conflict of interest.


  1. 1.
    World Health Organization: Blindness and vision impairment prevention. Available at: Accessed May 2018.
  2. 2.
    Bright Focus Foundation: Glaucoma: facts & figures. Available at: Accessed May 2018.
  3. 3.
    Kumar BN, Chauhan RP, Dahiya N: Detection of glaucoma using image processing techniques: A critique. Semin Ophthalmol 33(2):275–228, 2018Google Scholar
  4. 4.
    Almazroa A, Burman R, Raahemifar K, Lakshminarayanan V: Optic disc and optic cup segmentation methodologies for glaucoma image detection: A survey. Journal of ophthalmology 2015:1–28, 2015Google Scholar
  5. 5.
    Kavya N, Padmaja KV: Glaucoma detection using texture features extraction. Proceedings of the 51st IEEE Asilomar Conference on Signals, Systems, and Computers, 1471–1475, 2017.Google Scholar
  6. 6.
    Dey N et al.: Optical cup to disc ratio measurement for glaucoma diagnosis using harris corner. In: 3rd IEEE International Conference on Computing Communication & Networking Technologies (ICCCNT), pp. 1–5, 2012.Google Scholar
  7. 7.
    Dutta MK et al.: Glaucoma detection by segmenting the super pixels from fundus colour retinal images. In: International IEEE Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom), pp. 86–90, 2014.Google Scholar
  8. 8.
    Nath MK, Dandapat S: Differential entropy in wavelet subband for assessment of glaucoma. Int J Imaging Syst Technol 22(3):161–165, 2012Google Scholar
  9. 9.
    Nawaldgi S, Lalitha YS and Reddy M: A novel adaptive threshold and ISNT rule based automatic glaucoma detection from color fundus images. In: Satapathy S, Bhateja V, Raju K, Janakiramaiah B Eds. Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 542. Singapore: Springer, 2018.Google Scholar
  10. 10.
    Spaeth GL: Systems for staging the amount of optic nerve damage in glaucoma: A critical review and new material. Surv Ophthalmol 51(4):293–315, 2006Google Scholar
  11. 11.
    Bock R, Meier J, Nyúl LG, Hornegger J, Michelson G: Glaucoma risk index: Automated glaucoma detection from color fundus images. Med Image Anal 14(3):471–481, 2010Google Scholar
  12. 12.
    Thakur N, Juneja M: Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma. Biomed Signal Process Control 42:162–189, 2018Google Scholar
  13. 13.
    Youssif AA, Ghalwash AZ, Ghoneim AA: Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter. IEEE Trans Med Imaging 27(1):11–18, 2008Google Scholar
  14. 14.
    Bechar ME et al.: Semi-supervised superpixel classification for medical images segmentation: Application to detection of glaucoma disease. Multidim Syst Sign Process 29(3):979–998, 2018Google Scholar
  15. 15.
    Dua S, Acharya UR, Chowriappa P, Sree SV: Wavelet-based energy features for glaucomatous image classification. IEEE Trans Inf Technol Biomed 16(1):80–87, 2012Google Scholar
  16. 16.
    Singh A, Dutta MK, ParthaSarathi M, Uher V, Burget R: Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image. Comput Methods Prog Biomed 124:108–120, 2016Google Scholar
  17. 17.
    Dey A, Dey KN: Automated glaucoma detection from fundus images of eye using statistical feature extraction methods and support vector machine classification. In: Bhattacharyya S, Sen S, Dutta M, Biswas P, Chattopadhyay H Eds. Industry Interactive Innovations in Science, Engineering and Technology. Lecture Notes in Networks and Systems, vol 11. Singapore: Springer, 2018.Google Scholar
  18. 18.
    Akram MU, Tariq A, Khalid S, Javed MY, Abbas S, Yasin UU: Glaucoma detection using novel optic disc localization, hybrid feature set and classification techniques. Australas Phys Eng Sci Med 38(4):643–655, 2015Google Scholar
  19. 19.
    Vijapur NA, Kunte RSR: Sensitized glaucoma detection using a unique template based correlation filter and undecimated isotropic wavelet transform. J Med Biol Eng 37(3):365–373, 2017Google Scholar
  20. 20.
    Khalil T, Usman Akram M, Khalid S, Jameel A: Improved automated detection of glaucoma from fundus image using hybrid structural and textural features. IET Image Process 11(9):693–700, 2017Google Scholar
  21. 21.
    Mallat SG: A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693, 1989Google Scholar
  22. 22.
    Budai A, Bock R, Maier A, Hornegger J, Michelson G: Robust Vessel Segmentation in Fundus Images. Int J Biomed Imaging 2013:1–11, 2013Google Scholar
  23. 23.
    HRF Dataset website link: Accessed March 2018.
  24. 24.
    Jain AK: Fundamentals of digital image processing. Upper Saddle River: Prentice-Hall, Inc., 1989.Google Scholar
  25. 25.
    Zuiderveld K: Contrast limited adaptive histogram equalization. Chapter VIII.5, Graphics Gems IV. Heckbert PS Eds. Cambridge: Academic Press, 1994, pp 474–485.Google Scholar
  26. 26.
    Abdel-Hamid L, el-Rafei A, el-Ramly S, Michelson G, Hornegger J: Retinal image quality assessment based on image clarity and content. J Biomed Opt 21(9):96007, 2016Google Scholar
  27. 27.
    Abdel-Hamid L et al.: Performance dependency of retinal image quality assessment algorithms on image resolution: Analyses and solutions. SIViP 12(1):9–16, 2017Google Scholar
  28. 28.
    Maths Work: Regionprops. Available at Accessed May 2018.
  29. 29.
    Abdel-Hamid L, el-Rafei A, Michelson G: No-reference quality index for color retinal images. Computers in biology and medicine 90:68–75, 2017Google Scholar
  30. 30.
    Haralick RM, Shanmugam K: Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3(6):610–621, 1973Google Scholar
  31. 31.
    Conners RW: Segmentation of a high-resolution urban scene using texture operators. Comput Vision Graph Image Processing 25:273–310, 1984Google Scholar
  32. 32.
    Abdel-Hamid L: Glaucoma detection using statistical features: comparative study in RGB, HSV and CIEL*a*b* color model. Proceeding of the 10th SPIE International Conference on Graphic and Image Processing (ICGIP2018), In Press.Google Scholar
  33. 33.
    Coifman RR, Wickerhauser MV: Entropy-based algorithms for best basis selection. IEEE Trans. Inf. Theory 38(2):713–718, 1992Google Scholar
  34. 34.
    Kira K, Rendell LA: A practical approach to feature selection. Proceedings of the 9th International Workshop on Machine Learning, pp. 249–256, 1992.Google Scholar
  35. 35.
    Quinlan JR: C4.5: Programs for machine learning. San Francisco: Morgan Kaufmann Publishers, 1993.Google Scholar
  36. 36.
    Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH: The WEKA data mining software: An update. ACM SIGKDD Explor. Newsl. 11(1):10–18, 2009Google Scholar
  37. 37.
    Abdel Hamid LS et al.: No-reference wavelet based retinal image quality assessment. Proceedings of the 5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing (VipIMAGE), pp. 123–129, 2015.Google Scholar
  38. 38.
    Salam AA et al.: Benchmark data set for glaucoma detection with annotated cup to disc ratio. Proceedings of IEEE International Conference Signals and Systems (ICSigSys), pp. 227–233, 2017.Google Scholar
  39. 39.
    Kausu TR, Gopi VP, Wahid KA, Doma W, Niwas SI: Combination of clinical and multiresolution features for glaucoma detection and its classification using fundus images. Bioprocess Biosyst Eng 38(2):329–341, 2018Google Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2019

Authors and Affiliations

  1. 1.Electronics & Communications Department, Faculty of EngineeringMisr International UniversityCairoEgypt

Personalised recommendations