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Classification and Localisation of Diabetic-Related Eye Disease

Alireza Osareh, Majid Mirmehdi, Barry Thomas, Richard Markham , Classification and Localisation of Diabetic-Related Eye Disease. 7th European Conference on Computer Vision. A. Heyden, G. Sparr, M. Nielsen, P. Johansen, (eds.), pp. 502–516. May 2002. PDF, 662 Kbytes.


Retinal exudates are a characteristic feature of many retinal diseases such as Diabetic Retinopathy. We address the development of a method to quantitatively diagnose these random yellow patches in colour retinal images automatically. After a colour normalisation and contrast enhancement pre-processing step, the colour retinal image is segmented using Fuzzy C-Means clustering. We then classify the segmented regions into two disjoint classes, exudates and non-exudates, comparing the performance of various classifiers. We also locate the optic disk both to remove it as a candidate region and to measure its boundaries accurately since it is a significant landmark feature for ophthalmologists. Three different approaches are reported for optic disk localisation based on template matching, least squares arc estimation and snakes. The system could achieve an overall diagnostic accuracy of 90.1% for identification of the exudate pathologies and 90.7% for optic disk localisation.

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