Classification and Localisation of Diabetic-Related Eye Disease

A Osareh, M Mirmehdi, B Thomas, R Markham

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

110 Citations (Scopus)


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.
Translated title of the contributionClassification and Localisation of Diabetic-Related Eye Disease
Original languageEnglish
Title of host publicationUnknown
EditorsA. Heyden, G. Sparr, M. Nielsen, P. Johansen
PublisherSpringer Berlin Heidelberg
Pages502 - 516
Number of pages14
Publication statusPublished - May 2002

Bibliographical note

Conference Proceedings/Title of Journal: 7th European Conference on Computer Vision


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