Multifeature Detection of Microaneurysms Based on Improved SSA

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 mi...

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Bibliographic Details
Published in:Symmetry (Basel) 2021-11, Vol.13 (11), p.2147
Main Authors: Deng, Liwei, Wang, Xiaofei, Xu, Jiazhong
Format: Article
Language:English
Subjects:
Algorithms
asymmetric microaneurysm
Blood vessels
Clustering
connection components
Diabetes
diabetic retina
Diabetic retinopathy
Exploitation
Genetic algorithms
Mathematical models
Medical imaging
Methods
Optimization algorithms
Radon transform
SSA
Wavelet transforms
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Summary: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.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym13112147