Automated identification of diabetic retinal exudates in digital colour images

A Osareh, M Mirmehdi, B Thomas, R Markham

Research output: Contribution to journalArticle (Academic Journal)peer-review

215 Citations (Scopus)

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.
Translated title of the contributionAutomated identification of diabetic retinal exudates in digital colour images
Original languageEnglish
Pages (from-to)1220 - 1223
Number of pages3
JournalBritish Journal of Ophthalmology
Volume87(10)
Publication statusPublished - Oct 2003

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