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Locating the Optic Disk in Retinal Images

A. Osareh, M. Mirmehdi, B. Thomas, R. Markham, Locating the Optic Disk in Retinal Images. 2nd International Workshop on Computer Assisted Fundus Image Analysis. Bjarne Ersboll, (eds.), pp. 35–35. October 2001. No electronic version available.

Abstract

Aim: To provide an automatic method for the localisation of the optic disk in retinal images. Method: The location of the optic disk is an important issue in retinal image analysis as it is a significant landmark feature and its diameter is usually used as a reference length for measuring distances and sizes. In many cases, this process can be straightforward and a circular Hough transform or edge detection and watershed segmentation have been attempted. When there are other features in the retina images, such as Exudates (EXs), these may exhibit similar spectral characteristics to the optic disk. We have proposed a method to identify EXs automatically [1] that also partially extracts the optic disk as candidate EX regions due to colour similarity between the EXs and optic disk. This method is based on colour normalisation, contrast enhancement and colour segmentation based on Fuzzy C-Means (FCM) clustering. This partial localisation of the optic disk requires further processing to isolate it. In this paper, we report on the selection of candidate optic disk regions amongst the EXs, boundary analysis, and optic disk centre and radius estimation using minimum boundary arc lengths. Results: We applied our proposed method to 50 colour retinal images and the optic disk was identified correctly in all the images. Our method provides an accurate circular approximation of the optic disk region suitable for applications such as ours [1]. We intend to use a post-processing step for further, precise segmentation using "snakes" in future, as our method provides an automatic bootstrap snake spline for the process. Conclusion: In this work we introduce an efficient approach to accurately localise the optic disk. This robust technique will be used as part of our aim to establish a cost effective mass screening system for diagnosing diabetic maculopathy.

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