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The early diagnosis of retinopathy is crucial to the prevention and treatment of diabetic retinopathy. The low proportion of positive cases in the asymmetric microaneurysm detection problem causes preprocessing to treat microaneurysms as noise to be eliminated. To obtain a binary image containing microaneurysms, the object was segmented by a symmetry algorithm, which is a combination of the connected components and SSA methods. Next, a candidate microaneurysm set was extracted by multifeature clustering of binary images. Finally, the candidate microaneurysms were mapped to the Radon frequency domain to achieve microaneurysm detection. In order to verify the feasibility of the algorithm, a comparative experiment was conducted on the combination of the connected components and SSA methods. In addition, PSNR, FSIM, SSIM, fitness value, average CPU time and other indicators were used as evaluation standards. The results showed that the overall performance of the binary image obtained by the algorithm was the best. Last but not least, the accuracy of the detection method for microaneurysms in this paper reached up to 93.24%, which was better than that of several classic microaneurysm detection methods in the same period.

In recent years, the incidence of diabetes has been increasing year by year. Since most of the fundus lesions are located near blood vessels, the image information is complex, and the end vessels are difficult to identify. So, a new segmentation method of diabetic retinal vessel images based on particle swarm optimization and salp swarm algorithm is proposed. This paper uses a Gaussian filter to enhance the main blood vessels, and a top-bot hat transform is used to strengthen the end vessels. The preprocessing process is completed by combining and reconstructing the two images through a normalization operation. The improved particle swarm optimization and salp swarm algorithms perform multi-threshold segmentation on the preprocessed vessel images. The best fit value, Structural Similarity Index Measure, Peak Signal to Noise Rati, feature similarity index measure, sensitivity, accuracy, regional consistency, Dice coefficient, Jaccard similarity, and Shannon entropy are selected for comprehensive evaluation and analysis. The results showed that this paper’s improved particle swarm-salp swarm algorithm for segmenting diabetic retinal blood vessel images is more efficient, and the threshold is better. The vascular segmentation method in this paper is applied in medical image processing, which improves the accuracy of medical image processing and reduces the computational effort.