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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Autores principales:	Liwei Deng, Xiaofei Wang, Jiazhong Xu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	SSA connection components diabetic retina asymmetric microaneurysm Radon transform Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/1093af6c424c47d492010e9884173bdd
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spelling	oai:doaj.org-article:1093af6c424c47d492010e9884173bdd2021-11-25T19:07:08ZMultifeature Detection of Microaneurysms Based on Improved SSA10.3390/sym131121472073-8994https://doaj.org/article/1093af6c424c47d492010e9884173bdd2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-8994/13/11/2147https://doaj.org/toc/2073-8994The 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.Liwei DengXiaofei WangJiazhong XuMDPI AGarticleSSAconnection componentsdiabetic retinaasymmetric microaneurysmRadon transformMathematicsQA1-939ENSymmetry, Vol 13, Iss 2147, p 2147 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	SSA connection components diabetic retina asymmetric microaneurysm Radon transform Mathematics QA1-939
spellingShingle	SSA connection components diabetic retina asymmetric microaneurysm Radon transform Mathematics QA1-939 Liwei Deng Xiaofei Wang Jiazhong Xu Multifeature Detection of Microaneurysms Based on Improved SSA
description	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.
format	article
author	Liwei Deng Xiaofei Wang Jiazhong Xu
author_facet	Liwei Deng Xiaofei Wang Jiazhong Xu
author_sort	Liwei Deng
title	Multifeature Detection of Microaneurysms Based on Improved SSA
title_short	Multifeature Detection of Microaneurysms Based on Improved SSA
title_full	Multifeature Detection of Microaneurysms Based on Improved SSA
title_fullStr	Multifeature Detection of Microaneurysms Based on Improved SSA
title_full_unstemmed	Multifeature Detection of Microaneurysms Based on Improved SSA
title_sort	multifeature detection of microaneurysms based on improved ssa
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1093af6c424c47d492010e9884173bdd
work_keys_str_mv	AT liweideng multifeaturedetectionofmicroaneurysmsbasedonimprovedssa AT xiaofeiwang multifeaturedetectionofmicroaneurysmsbasedonimprovedssa AT jiazhongxu multifeaturedetectionofmicroaneurysmsbasedonimprovedssa
_version_	1718410260851458048

Multifeature Detection of Microaneurysms Based on Improved SSA

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