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  • Title: Automated identification of diabetic retinal exudates in digital colour images.
    Author: Osareh A, Mirmehdi M, Thomas B, Markham R.
    Journal: Br J Ophthalmol; 2003 Oct; 87(10):1220-3. PubMed ID: 14507751.
    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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