Colloquia, Seminars and Conference News
Title : Optic Disc Detection from Normalized Digital Fundus Images
Date : August 21, 2007. (1:30 pm) Tea starts half an hour before each seminar
Location: ITEB 336
Speaker : Prof. Aliaa Youssif
Abstract:
Diabetes is a disease that affects about 5.5% of the global population. In Egypt, nearly 9 million (over 13% of the population = 20 years) will have diabetes by the year 2025, while recent surveys from Oman and Pakistan suggest that this may be a regional phenomenon. Consequently, about 10% of all diabetic patients have diabetic retinopathy (DR); one of the most prevalent complications of diabetes and which is the primary cause of blindness in the Western World, and this is likely to be true in Hong Kong and Egypt. Moreover, diabetic population is expected to have a 25 times greater risk of going blind than non-diabetic. Due to the growing number of patients, and with insufficient ophthalmologists to screen them all, automatic screening can reduce the threat of blindness by 50%, provide considerable cost savings, and decrease the pressure on available infrastructures and resources.
Optic Disc (OD) detection is a main step while developing automated screening systems for diabetic retinopathy. This presentation introduces a method to automatically detect the position of the OD in digital retinal fundus images. The method starts by normalizing luminosity and contrast through out the image using illumination equalization and adaptive histogram equalization methods respectively. The OD detection algorithm is based on matching the expected directional pattern of the retinal blood vessels. Hence, a simple matched filter is proposed to roughly match the direction of the vessels at the OD vicinity. The retinal vessels are segmented using a simple and standard 2D Gaussian matched filter.
Consequently, a vessels direction map of the segmented retinal vessels is obtained using the same segmentation algorithm. The segmented vessels are then thinned, and filtered using local intensity, to represent finally the OD-center candidates. The difference between the proposed matched filter resized into four different sizes, and the vessels directions at the surrounding area of each of the OD-center candidates is measured. The minimum difference provides an estimate of the OD-center coordinates. The proposed method was evaluated using a subset of the STARE project's dataset, containing 81fundus images of both normal and diseased retinas, and initially used by literature OD detection methods. The OD-center was detected correctly in 80 out of the 81 images (98.77%). In addition, the OD-center was detected correctly in all of the 40 images (100%) using the publicly available DRIVE dataset.
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