@article{Osareh_BJO,
author={A Osareh and  M Mirmehdi and  B Thomas and R Markham},
title={Automated identification of diabetic retinal exudates in digital colour images},
journal={British Journal of Ophthalmology},
ISSN={0007-1161},
volume={87},
number={10},
pages={1220--1223},
month={October},
year={2003},
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. },
abstract-url={http://www.cs.bris.ac.uk/Publications/pub_master.jsp?id=2000035},
url={http://www.cs.bris.ac.uk/Publications/Papers/2000035.pdf},
keyword={Computer Vision},
pubtype={101}
}