An Approach for the Early Detection of Retinal Disorders and Performing Human Authentication

  • G. R. PrashanthaEmail author
  • Chandrashekar M. Patil
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 14)


Diabetes is a serious disease which is caused due to the high blood sugar level or in other words due to the reduced insulin production in the body. Prolonged diabetes affects the blood vessels present in the eye and is termed as Diabetic Retinopathy. Diabetic Maculopathy is one such disease suffered by retinopathic patients which results when the fluid leaks out from the blood vessels that are damaged and gather near the central region of retina called Macula. In this paper an approach to detect abnormalities such as blood vessels, micro aneurysms exudates using image processing techniques in the fundus image. These features are used for the detection of severity of Diabetic Retinopathy, Diabetic maculopathy. Authentication of a person can be done based on matching the extracted vessel pattern of the retina with the reference. The algorithm so presented detects the Diabetic Retinopathy and classifies it according to its severity levels. It also detects Maculopathy at the early stage of the disease and performs the authentication of a person based on the blood vessel pattern matching. This system intend to help ophthalmologist in the screening process to detect symptoms of diabetic retinopathy, diabetic maculopathy quickly and more easily. The proposed algorithm is tested over 4 different databases. The multiclass SVM classifies the input retinal image into different classes of disorder as severe, mild, moderate diabetic retinopathy, healthy and maculopathy. The classification is done based on the color matching and SVM classifier by calculating the average intensity, variance, standard deviation, median and centroid. The algorithm is tested over the readily available DRIVE, HRF, DIARETDB0 and DIARETDB1 databases.


Diabetic retinopathic Diabetic maculopathy Macula SVM 


  1. 1.
    Hoover A, Kouznetsova V, Goldbaum (1998) Locating blood vessels in retinal image by piecewise threshold probing of matched filter response. IEEE Trans Med Imaging 19(3)Google Scholar
  2. 2.
    Chanwimaluang T, Fan G (2003) An efficient algorithm for extraction of anatomical structures in retinal images. In: IEEE international conference on image processing, 2003Google Scholar
  3. 3.
    Katia E, Figueiredo RJ (2007) Automatic detection and diagnosis of diabetic retinopathy. IEEE international conference on image processing, vol 2, 2007Google Scholar
  4. 4.
    Fukuta K, Nakagawa T, Hayashi Y, Hatanaka Y, Hara T, Fujita H (2008) Personal identification based on blood vessels of retinal fundus images. In: Proceedings of International Society for Optical Engineering, 2008Google Scholar
  5. 5.
    Ardizzone E, Pirrone R, Gambino O, Radosta S (2008) Blood vessels and feature points detection on retinal images. In: 30th annual international IEEE EMBS conference, 2008Google Scholar
  6. 6.
    Faust O, Acharya R, Hoong K, Suri JS (2010) Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review Google Scholar
  7. 7.
    Siddalingaswamy PC, Prabhu GK (2010) Automatic grading of diabetic maculopathy severity levels. In: IEEE proceedings of 2010 international conference on systems in medicine and biology (ICSMB), pp 331–334, 2010Google Scholar
  8. 8.
    Kumari VV, Suriyanarayanan N, Saranya CT (2010) Feature extraction for early detection of diabetic retinopathy. In: International conference on recent trends in information, telecommunication and computing, IEEE, 2010Google Scholar
  9. 9.
    Latha L, Pabithaand M, Thangasamy S (2010) Effectual human authentication for critical security applications using retinal images. ICTACT J Image Video ProcessGoogle Scholar
  10. 10.
    Hunter A, Lowell JA, Ryder B, Basu A, Steel D (2011) Automated diagnosis of referable maculopathy in diabetic retinopathy screening. In: 33rd annual international conference of the IEEE EMBS, pp. 3375–3378, IEEE, Boston, Mass, USA, September 2011 97–105, 2011Google Scholar
  11. 11.
    Oloumi F, Rangayyan RM, Ells AL (2012) Computer aided diagnosis of proliferative diabetic retinopathy. In: 34th annual international conference of the IEEE EMBS 2012Google Scholar
  12. 12.
    Punnolil A (2013) A novel approach for diagnosis and severity grading of diabetic maculopathy. IEEE Trans Med ImagingGoogle Scholar
  13. 13.
    Datta NS, Dutta HS, De M, Mondal S (2013) An efficient approach: image quality enhancement for microaneurysms detection of non-dilated retinal fundus imageGoogle Scholar
  14. 14.
    Shikarwar S, Rathod D, Diwanji H (2014) Review paper on retina authentication and its security issues. Int J Technol Res Eng(IJTRE), 2014Google Scholar
  15. 15.
    Medhi JP, Dandapat S (2014) Analysis of maculopathy in color fundus images. In: Annual IEEE India conference 2014Google Scholar
  16. 16.
    Rahim SS, Palade V, Jayne C, Holzinger A, Shuttleworth J (2015) Detection of diabetic retinopathy and maculopathy in eye fundus images using fuzzy image processing. Brain Informatics and Health (BIH) 2015. Lecture Notes in Computer Science, vol 9250. Springer, ChamGoogle Scholar
  17. 17.
    Tobin KW, Abramoff MD, Chaum E, Giancardo L, Govindasamy VP, Karnowski TP, Tennant MT, Swainson S (2008) Using a patient image archive to diagnose retinopathy. In: 30th annual international IEEE EMBS conference, 2008 Google Scholar
  18. 18.
    Antal B, Hajdu A (2012) An ensemble based system for microaneurysm detection and diabetic retinopathy grading. IEEE Trans Biomed Eng 59(6):1720–1726, June 2012Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of ISESKAVMACETLaxmeshwarIndia
  2. 2.Department of ECEVVCEMysoreIndia

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