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

TL;DR

This paper introduces a novel automated method using Radon transform and multi-overlapping windows for detecting microaneurysms in fluorescein angiography images to aid early diabetic retinopathy diagnosis.

Contribution

It presents a new algorithm combining Radon transform with image segmentation techniques for improved microaneurysm detection in retinal images.

Findings

Achieved 94% sensitivity on Mashhad database.

Achieved 100% sensitivity on Tehran database.

Demonstrated effective detection across multiple datasets.

Abstract

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