| Summary: | Diabetic retinopathy (DR) is one of the most common microvascular complications and its early detection is critical for the prevention of vision loss. Recent studies have indicated that microaneurysms (MAs) are the hallmark of DR. However, the detection of MAs relies on trained clinicians and relatively expensive software. Moreover, manual errors often lower the accuracy of this detection. Therefore, an automatic analysis technique is highly demanded in the detection of DR progression. In this paper, we present a novel and complete methodology involving two different ways from the view of MAs turnover and pathological risk factors to diagnose the progression of DR. Specifically, one approach follows the traditional image analysis-based roadmap to obtain MAs turnover. The other investigates seven pathological features, related with MAs turnover, to classify the unchanged, new, and resolved MAs by means of statistical analysis and pattern classification techniques. The evaluations on Grampian diabetes database show that the proposed image analysis method could achieve a sensitivity of 94% and a specificity of 93%, while the classification model could achieve 89% sensitivity and 88% specificity, respectively. We also analyzed the potential weight of pathological risk factors leading to the MAs turnover, which could provide an alternative guidance for the progression of DR than traditional detection methods. In conclusion, this study provides a novel and noninvasive detection technique for early diagnosis of diabetic retinopathy with a competitive accuracy.
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