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Automated identification of diabetic retinal exudates in digital colour images

A Osareh, M Mirmehdi, B Thomas, R Markham, Automated identification of diabetic retinal exudates in digital colour images. British Journal of Ophthalmology, 87(10). ISSN 0007-1161, pp. 1220–1223. October 2003. PDF, 1836 Kbytes.

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.

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