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Early detection of microaneurysms (MAs), the first sign of DiabeticRetinopathy (DR), is an essential first step in automated detection of DR toprevent vision loss and blindness. This study presents a novel and differentalgorithm for automatic detection of MAs in fluorescein angiography (FA) fundusimages, based on Radon transform (RT) and multi-overlapping windows. Thisproject addresses a novel method, in detection of retinal land marks andlesions to diagnose the DR. At the first step, optic nerve head (ONH) wasdetected and masked. In preprocessing stage, top-hat transformation andaveraging filter were applied to remove the background. In main processingsection, firstly, we divided the whole preprocessed image into sub-images andthen segmented and masked the vascular tree by applying RT in each sub-image.After detecting and masking retinal vessels and ONH, MAs were detected andnumbered by using RT and appropriated thresholding. The results of the proposedmethod were evaluated reported on three different retinal images databases, theMashhad Database with 120 FA fundus images, Second Local Database from Tehranwith 50 FA retinal images and a part of Retinopathy Online Challenge (ROC)database with 22 images. Automated DR detection demonstrated a sensitivity andspecificity of 94% and 75% for Mashhad database and 100% and 70% for the SecondLocal Database respectively.

A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy