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Multiscale Joint Optimization Strategy for Retinal Vascular Segmentation
The accurate segmentation of retinal vascular is of great significance for the diagnosis of diseases such as diabetes, hypertension, microaneurysms and arteriosclerosis. In order to segment more deep and small blood vessels and provide more information to doctors, a multi-scale joint optimization st...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838406/ https://www.ncbi.nlm.nih.gov/pubmed/35162002 http://dx.doi.org/10.3390/s22031258 |
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author | Yan, Minghan Zhou, Jian Luo, Cong Xu, Tingfa Xing, Xiaoxue |
author_facet | Yan, Minghan Zhou, Jian Luo, Cong Xu, Tingfa Xing, Xiaoxue |
author_sort | Yan, Minghan |
collection | PubMed |
description | The accurate segmentation of retinal vascular is of great significance for the diagnosis of diseases such as diabetes, hypertension, microaneurysms and arteriosclerosis. In order to segment more deep and small blood vessels and provide more information to doctors, a multi-scale joint optimization strategy for retinal vascular segmentation is presented in this paper. Firstly, the Multi-Scale Retinex (MSR) algorithm is used to improve the uneven illumination of fundus images. Then, the multi-scale Gaussian matched filtering method is used to enhance the contrast of the retinal images. Optimized by the Particle Swarm Optimization (PSO) algorithm, Otsu algorithm (OTSU) multi-threshold segmentation is utilized to segment the retinal image extracted by the multi-scale matched filtering method. Finally, the image is post-processed, including binarization, morphological operation and edge-contour removal. The test experiments are implemented on the DRIVE and STARE datasets to evaluate the effectiveness and practicability of the proposed method. Compared with other existing methods, it can be concluded that the proposed method can segment more small blood vessels while ensuring the integrity of vascular structure and has a higher performance. The proposed method has more obvious targets, a higher contrast, more plentiful detailed information, and local features. The qualitative and quantitative analysis results show that the presented method is superior to the other advanced methods. |
format | Online Article Text |
id | pubmed-8838406 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88384062022-02-13 Multiscale Joint Optimization Strategy for Retinal Vascular Segmentation Yan, Minghan Zhou, Jian Luo, Cong Xu, Tingfa Xing, Xiaoxue Sensors (Basel) Article The accurate segmentation of retinal vascular is of great significance for the diagnosis of diseases such as diabetes, hypertension, microaneurysms and arteriosclerosis. In order to segment more deep and small blood vessels and provide more information to doctors, a multi-scale joint optimization strategy for retinal vascular segmentation is presented in this paper. Firstly, the Multi-Scale Retinex (MSR) algorithm is used to improve the uneven illumination of fundus images. Then, the multi-scale Gaussian matched filtering method is used to enhance the contrast of the retinal images. Optimized by the Particle Swarm Optimization (PSO) algorithm, Otsu algorithm (OTSU) multi-threshold segmentation is utilized to segment the retinal image extracted by the multi-scale matched filtering method. Finally, the image is post-processed, including binarization, morphological operation and edge-contour removal. The test experiments are implemented on the DRIVE and STARE datasets to evaluate the effectiveness and practicability of the proposed method. Compared with other existing methods, it can be concluded that the proposed method can segment more small blood vessels while ensuring the integrity of vascular structure and has a higher performance. The proposed method has more obvious targets, a higher contrast, more plentiful detailed information, and local features. The qualitative and quantitative analysis results show that the presented method is superior to the other advanced methods. MDPI 2022-02-07 /pmc/articles/PMC8838406/ /pubmed/35162002 http://dx.doi.org/10.3390/s22031258 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yan, Minghan Zhou, Jian Luo, Cong Xu, Tingfa Xing, Xiaoxue Multiscale Joint Optimization Strategy for Retinal Vascular Segmentation |
title | Multiscale Joint Optimization Strategy for Retinal Vascular Segmentation |
title_full | Multiscale Joint Optimization Strategy for Retinal Vascular Segmentation |
title_fullStr | Multiscale Joint Optimization Strategy for Retinal Vascular Segmentation |
title_full_unstemmed | Multiscale Joint Optimization Strategy for Retinal Vascular Segmentation |
title_short | Multiscale Joint Optimization Strategy for Retinal Vascular Segmentation |
title_sort | multiscale joint optimization strategy for retinal vascular segmentation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838406/ https://www.ncbi.nlm.nih.gov/pubmed/35162002 http://dx.doi.org/10.3390/s22031258 |
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