Diabetes is a disease which occurs when the pancreas does not secrete enough insulin or the body is unable to process it properly. This disease affects slowly the circulatory system including that of the retina. As diabetes progresses, the vision of a patient may start to deteriorate and lead to diabetic retinopathy. Diabetic retinopathy is most common occurring disease in the world. The features are extracted from the raw images using the image processing techniques. Since many features have common intensity properties, geometric features and correlations are used to distinguish between them. We also show that many of the features such as the blood vessels, exudates and microaneurysms and hemorrhages can be detected quite accurately. The detection of blood vessels from the retinal images is usually a tedious process. In this work a new algorithm to detect the blood vessels effectively has been proposed. The initial enhancement of the image is carried out using Adaptive Histogram Equalization. This enhanced image is used for the extraction of the blood vessels. The vessel extraction is done based on thresholding technique and the Kirsch's templates. It involves spatial filtering of the image using the templates in eight different orientations.

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Diabetic Retinopathy Retinal Image Adaptive Histogram Equalization And Kirsch's Template