Detection of microaneurysms in color fundus images based on local Fourier transform

•The images are divided into isolated smaller images for local Fourier transform.•The local Fourier transform can work better with non-stationary signals.•The statistical features can describe the cross-section well.•The extracted features can achieve a better classification result. Retinal microaneurysm (MA) is one of the early clinical signs of diabetic retinopathy (DR), which is essential for the early diagnosis of DR. Its shape is often shown as a red dot, which makes detection of MA challenging because of its tiny size. There are many effective MA automatic detection methods having been proposed, but these methods are hard to describe the overall shape of Mas. This paper proposes an automatic MA detection method based on local Fourier transform which splits the original image into small independent images and calculates their local Fourier transforms in isolation. After that, the object is not the whole image but a series of independent small images. We also extract a statistical feature to check for normality and design a new feature to represent the difference among every direction. After that, a group of candidate points are extracted from the images. Finally, some features are extracted from the candidate points, and the confidence scores of the candidate points are obtained by using random forest algorithm for classification. The proposed method obtained a FROC curve score of 0.847 on the E-Ophtha MA database, which is better than most existing methods. In the ROC database, although the FROC curve score is only 0.283, the AUC can reach 0.961, which is better than most methods. It can be seen that the proposed method can achieve a better result with low noise and large sample size.