Segmentation of retinal microaneurysms in fluorescein fundus angiography images by a novel three-step model - Li, Jing; Ma, Qian; Yao, Mudi; Jiang, Qin; Wang, Zhenhua; Yan, Biao (Frontiers in Medicine, 2024)
Microaneurysms serve as early signs of diabetic retinopathy and their accurate detection is critical for effective treatment of diabetic retinopathy. However, due to their low contrast and similarity to retinal vessels, distinguishing microaneurysms from background noise and retinal vessels in fluorescein fundus angiography images poses a significant challenge. In this study, we present a model for the automatic detection of microaneurysms. Fluorescein fundus angiography images were pre-processed using Top-hat transformation, Gray-stretching, and Gaussian filter techniques to eliminate noises. Subsequently, the candidate microaneurysms were coarsely segmented using an improved matched filter algorithm. Real microaneurysms were then segmented by a morphological strategy. To evaluate the segmentation performance, our proposed model was compared against other models including OSTU, Region Growing, Global Threshold, Matched Filter, Fuzzy c-means, and K-means, using both self-constructed and publicly available datasets. Performance metrics such as accuracy, sensitivity, specificity, positive predictive value, and intersection-over-union were calculated. Our results indicate that the proposed model outperforms other models in terms of accuracy, sensitivity, specificity, positive predictive value, and intersection-over-union. Furthermore, the segmentation results obtained with our model closely align with the ground truth. In conclusion, our model demonstrates significant advantages for microaneurysm segmentation in fluorescein fundus angiography images and holds promise for clinical application in diagnosis of diabetic retinopathy.