The uniqueness of retinal microvasculature is that it is the only part of human circulation that can be directly visualised non-invasively in vivo and readily photographed. Developments in fundus image processing over the past 20 years includes advancement being made towards developing automated detection for conditions, such as diabetic retinopathy, age-related macular degeneration and retinopathy of prematurity. Features of retinal blood vessels, microaneurysms, exudates and the hemorrhages are extracted to detect the Diabetic Retinopathy (DR) in the early stages. Diabetic Retinopathy results fluid leaks from retinal blood vessels leading to vision loss. Microaneurysms appear as small circular dark spots on the surface of the retina. The appearance of red and yellow lesions on retina is exudates and hemorrhages. Image processing algorithms can be used to reduce the workload of ophthalmologist and play a vital role in quality assurance tasks. Feature extraction is the first step in developing these automated algorithms for detecting retinal pathologies. Here we review numerous early studies that used for automatic detection of these features. Most of the literature has differences in the method used to evaluate their algorithms or the dataset used, which makes it difficult to compare any two algorithms together. Our study reveals that even though a large number of feature extraction technique are available there is still scope for more accurate algorithms which will work with High Resolution Fundus (HRF) images also.

Diabetic retinopathy, Exudates, Hemorrhages, Micro aneurysms

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