Journal of Medical Systems

, Volume 32, Issue 2, pp 107–115

Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Images

  • Jagadish Nayak
  • P Subbanna Bhat
  • Rajendra Acharya U
  • C. M. Lim
  • Manjunath Kagathi
Original Paper

DOI: 10.1007/s10916-007-9113-9

Cite this article as:
Nayak, J., Bhat, P.S., Acharya U, R. et al. J Med Syst (2008) 32: 107. doi:10.1007/s10916-007-9113-9

Abstract

Diabetic retinopathy (DR) is caused by damage to the small blood vessels of the retina in the posterior part of the eye of the diabetic patient. The main stages of diabetic retinopathy are non-proliferate diabetes retinopathy (NPDR) and proliferate diabetes retinopathy (PDR). The retinal fundus photographs are widely used in the diagnosis and treatment of various eye diseases in clinics. It is also one of the main resources for mass screening of diabetic retinopathy. In this work, we have proposed a computer-based approach for the detection of diabetic retinopathy stage using fundus images. Image preprocessing, morphological processing techniques and texture analysis methods are applied on the fundus images to detect the features such as area of hard exudates, area of the blood vessels and the contrast. Our protocol uses total of 140 subjects consisting of two stages of DR and normal. Our extracted features are statistically significant (p < 0.0001) with distinct mean ± SD as shown in Table 1. These features are then used as an input to the artificial neural network (ANN) for an automatic classification. The detection results are validated by comparing it with expert ophthalmologists. We demonstrated a classification accuracy of 93%, sensitivity of 90% and specificity of 100%.

Keywords

Retinopathy Fundus images Exudates Retinal blood vessels Image morphology Artificial neural network 

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Jagadish Nayak
    • 1
  • P Subbanna Bhat
    • 2
  • Rajendra Acharya U
    • 3
  • C. M. Lim
    • 3
  • Manjunath Kagathi
    • 4
  1. 1.Department of E&C EngineeringManipal Institute of TechnologyManipalIndia
  2. 2.Department of E&C EngineeringNational Institute of Technology KarnatakaMangaloreIndia
  3. 3.Department of ECENgee Ann PolytechnicClementiSingapore
  4. 4.Eye CentreNational University HospitalSingaporeSingapore

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