The legal cause of blindness for the workingage population in western countries is Diabetic Retinopathy - a complication of diabetes mellitus - is a severe and wide- spread eye disease. Digital color fundus images are becoming increasingly important for the diagnosis of Diabetic Retinopathy. In order to facilitate and improve diagnosis in different ways, this fact opens the possibility of applying image processing techniques .Microaneurysms is the earliest sign of DR, therefore an algorithm able to automatically detect the microaneurysms in fundus image captured. Since microaneurysms is a necessary preprocessing step for a correct diagnosis. Some methods that address this problem can be found in the literature but they have some drawbacks like accuracy or speed. The aim of this thesis is 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 by using extra neurons which is evaluated on DRIVE database which is superior than rulebased methods. To identify microaneurysms in an image morphological opening and image enhancement is performed. The complete algorithm is developed by using a MATLAB implementation and the diagnosis in an image can be estimated with the better accuracy and in shorter time than previous techniques.