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Clinical science
Scientific reports
Automated identification of diabetic retinal exudates in digital colour images
  1. A Osareh1,
  2. M Mirmehdi1,
  3. B Thomas1,
  4. R Markham2
  1. 1Department of Computer Science, University of Bristol, Bristol BS8 1UB, UK
  2. 2Bristol Eye Hospital, Bristol BS1 2LX, UK
  1. Correspondence to: Mr Alireza Osareh, Bristol University, Merchant Ventures Building, Woodland Road, Bristol BS8 1UB, UK; a.osareh{at}bristol.ac.uk

Abstract

Aim: To identify retinal exudates automatically from colour retinal images.

Methods: The colour retinal images were segmented using fuzzy C-means clustering following some key preprocessing steps. To classify the segmented regions into exudates and non-exudates, an artificial neural network classifier was investigated.

Results: The proposed system can achieve a diagnostic accuracy with 95.0% sensitivity and 88.9% specificity for the identification of images containing any evidence of retinopathy, where the trade off between sensitivity and specificity was appropriately balanced for this particular problem. Furthermore, it demonstrates 93.0% sensitivity and 94.1% specificity in terms of exudate based classification.

Conclusions: This study indicates that automated evaluation of digital retinal images could be used to screen for exudative diabetic retinopathy.

  • diabetic retinopathy
  • exudates
  • segmentation
  • neural networks

https://doi.org/10.1136/bjo.87.10.1220

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