Automated Glaucoma Detection Using Global Statistical Parameters of Retina Fundus Images

  • Prathiksha R. Puthren
  • Ayush AgrawalEmail author
  • Usha PadmaEmail author
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Glaucoma is an eye disorder which is prevalent in the ageing population and causes irreversible loss of vision. Hence, computer-aided solutions are of interest for screening purposes. Glaucoma is indicated by structural changes in the Optic Disc (OD), loss of nerve fibres and atrophy of the peripapillary region of optic disc in retina. In retina images, most changes appear in form of subtle variation in appearance. Hence, automated assessment of glaucoma from colour fundus images is a challenging problem. Prevalent approaches aim at detecting the primary indicator, namely, the optic cup deformation relative to the disc and use the ratio of the two diameters in the vertical direction, to classify images as normal or glaucomatous. An attempt is made to detect glaucoma by combining image processing and neural network techniques. The risk of blindness can be reduced by 50% with screening patients vulnerable to eye diseases specially glaucoma. The global statistical features of the dataset images are used to detect images as glaucoma or normal. The technique involves screening for the vital signs such as intensity values in the fundus image for detecting glaucoma in patients. The result shows the feasibility of detection of glaucoma for vulnerable patient.


Glaucoma detection Retina fundus image Neural networks Backpropagation neural network 



This work was supported by Department of Telecommunication, R.V. College of Engineering, Bangalore, India. The authors would like to thank the Department for providing excellent facilities and timely guidance throughout the completion of the project.


  1. 1.
    Naveen Kumar B, Chauhan RP, Dahiya N (2016) Detection of Glaucoma using Image processing techniques: a review. IEEE Trans Biomed Eng 62(5)Google Scholar
  2. 2.
    Sheeba O, George J, Rajin PK, Thomas N, George S (2014) Glaucoma detection using artificial neural network. IACSIT Int J Eng Technol 6(2):158CrossRefGoogle Scholar
  3. 3.
    Dey N, Roy AB, Das A, Choudari S (2012) Optical cup to disc ratio measurement for glaucoma diagnosis using harris corner. Eur J Sci Res 59. ISSN 1450-217XGoogle Scholar
  4. 4.
    Narasimhan K, Vijayarekha K, JogiNarayana KA, SivaPrasad P, Satish Kumar V (2011) Glaucoma detection from fundus image using opencv. Res J Appl Sci Eng Technol 62(5)Google Scholar
  5. 5.
    Ganesh Babu TR, Shenbagadevi S (2011) Automatic detection of glaucoma using fundus image. Eur J Sci Res. 59(1):22–32. ISSN 1450-216XGoogle Scholar
  6. 6.
    Zhang Z, Liu J, Cherian SN, Sun Y, Lim JH, Wong WK, Tan NM, Lu S, Li H, Wong TY (2009) Convex hull based optic cup ellipse optimization in glaucoma diagnosis. In: 31st annual international conference of the IEEE EMBS, Minneapolis, Minnesota, USA, 2–6 Sept 2009Google Scholar
  7. 7.
    Inoue N, Yanashima K, Magatani K, Kurihara T (2005) Development of a simple diagnostic method for the glaucoma using ocular fundus pictures. In: Proceedings of the 2005 IEEE engineering in medicine and biology 27th annual conference Shanghai, China, 1–4 Sept 2005Google Scholar
  8. 8.
    Li H, Chutatape O (2003) A model-based approach for automated feature extraction in fundus images. In: Proceedings of the ninth IEEE international conference on computer vision (ICCV 2003) 2-Volume Set 0-7695-1950- 4/03Google Scholar
  9. 9.
    High Resolution Fundus Image database.
  10. 10.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Telecommunication EngineeringRV College of EngineeringBengaluruIndia

Personalised recommendations