Diabetic Retinopathy - a complication of diabetes mellitus - is a severe and wide-spread eye disease: it is the leading cause of legal blindness for the working age population in western countries. For the diagnosis of Diabetic Retinopathy, digital color fundus images are becoming increasingly important. This fact opens the possibility of applying image processing techniques in order to facilitate and improve diagnosis in different ways. Since microaneurysms are earliest sign of DR, therefore an algorithm able to automatically detect the microaneurysms in fundus image captured is a necessary preprocessing step for a correct diagnosis. This paper aims to develop and test a new method for detecting the microaneurysms in retina images. To do so preprocessing, gray level 2D feature based vessel extraction is done using neural network. The method is evaluated on DRIVE database and average accuracy of 0.9361 is obtained which is superior than other rule based methods in literature. To identify microaneurysms in an image morphological opening and image enhancement operations are performed. A MATLAB implementation of the complete algorithm is developed and tests suggest that the diagnosis in an image can be estimated in shorter time than previous techniques with the same or better accuracy.
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