Diabetic retinopathy (DR) is a diabetes complication, which in extreme situations may lead to blindness. Since the first stages are often asymptomatic, regular eye examinations are required for an early diagnosis. As microaneurysms (MAs) are one of the first signs of DR, several automated methods have been proposed for their detection in order to reduce the ophthalmologists’ workload. Although local convergence filters (LCFs) have already been applied for feature extraction, their potential as MA enhancement operators was not explored yet. In this work, we propose a sliding band filter for MA enhancement aiming at obtaining a set of initial MA candidates. Then, a combination of the filter responses with color, contrast and shape information is used by an ensemble of classifiers for final candidate classification. Finally, for each eye fundus image, a score is computed from the confidence values assigned to the MAs detected in the image. The performance of the proposed methodology was evaluated in four datasets. At the lesion level, sensitivities of 64% and 81% were achieved for an average of 8 false positives per image (FPIs) in e-ophtha MA and SCREEN-DR, respectively. In the last dataset, an AUC of 0.83 was also obtained for DR detection.

糖尿病性视网膜病（DR）是一种糖尿病并发症，在极端情况下可能会导致失明。由于第一阶段通常是无症状的，因此需要定期进行眼部检查以进行早期诊断。由于微动脉瘤（MA）是DR的先兆之一，因此提出了几种自动检测方法，以减少眼科医生的工作量。尽管已经将局部收敛滤波器（LCF）应用于特征提取，但尚未探索其作为MA增强运算符的潜力。在这项工作中，我们提出了一种用于MA增强的滑带滤波器，旨在获得一组初始MA候选对象。然后，一组分类器将滤波器响应与颜色，对比度和形状信息的组合用于最终候选分类。最后，对于每个眼底图像，根据分配给图像中检测到的MA的置信度值计算得分。在四个数据集中评估了所提出方法的性能。在病变水平上，在e-ophtha MA和SCREEN-DR中，平均每幅图像8个假阳性（FPI）的敏感性分别达到64％和81％。在最后一个数据集中，用于DR检测的AUC也为0.83。