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Automatic non-proliferative diabetic retinopathy screening system based on color fundus image

BACKGROUND: Non-proliferative diabetic retinopathy is the early stage of diabetic retinopathy. Automatic detection of non-proliferative diabetic retinopathy is significant for clinical diagnosis, early screening and course progression of patients. METHODS: This paper introduces the design and implem...

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Autores principales: Xiao, Zhitao, Zhang, Xinpeng, Geng, Lei, Zhang, Fang, Wu, Jun, Tong, Jun, Ogunbona, Philip O., Shan, Chunyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5659045/
https://www.ncbi.nlm.nih.gov/pubmed/29073912
http://dx.doi.org/10.1186/s12938-017-0414-z
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author Xiao, Zhitao
Zhang, Xinpeng
Geng, Lei
Zhang, Fang
Wu, Jun
Tong, Jun
Ogunbona, Philip O.
Shan, Chunyan
author_facet Xiao, Zhitao
Zhang, Xinpeng
Geng, Lei
Zhang, Fang
Wu, Jun
Tong, Jun
Ogunbona, Philip O.
Shan, Chunyan
author_sort Xiao, Zhitao
collection PubMed
description BACKGROUND: Non-proliferative diabetic retinopathy is the early stage of diabetic retinopathy. Automatic detection of non-proliferative diabetic retinopathy is significant for clinical diagnosis, early screening and course progression of patients. METHODS: This paper introduces the design and implementation of an automatic system for screening non-proliferative diabetic retinopathy based on color fundus images. Firstly, the fundus structures, including blood vessels, optic disc and macula, are extracted and located, respectively. In particular, a new optic disc localization method using parabolic fitting is proposed based on the physiological structure characteristics of optic disc and blood vessels. Then, early lesions, such as microaneurysms, hemorrhages and hard exudates, are detected based on their respective characteristics. An equivalent optical model simulating human eyes is designed based on the anatomical structure of retina. Main structures and early lesions are reconstructed in the 3D space for better visualization. Finally, the severity of each image is evaluated based on the international criteria of diabetic retinopathy. RESULTS: The system has been tested on public databases and images from hospitals. Experimental results demonstrate that the proposed system achieves high accuracy for main structures and early lesions detection. The results of severity classification for non-proliferative diabetic retinopathy are also accurate and suitable. CONCLUSIONS: Our system can assist ophthalmologists for clinical diagnosis, automatic screening and course progression of patients.
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spelling pubmed-56590452017-11-01 Automatic non-proliferative diabetic retinopathy screening system based on color fundus image Xiao, Zhitao Zhang, Xinpeng Geng, Lei Zhang, Fang Wu, Jun Tong, Jun Ogunbona, Philip O. Shan, Chunyan Biomed Eng Online Research BACKGROUND: Non-proliferative diabetic retinopathy is the early stage of diabetic retinopathy. Automatic detection of non-proliferative diabetic retinopathy is significant for clinical diagnosis, early screening and course progression of patients. METHODS: This paper introduces the design and implementation of an automatic system for screening non-proliferative diabetic retinopathy based on color fundus images. Firstly, the fundus structures, including blood vessels, optic disc and macula, are extracted and located, respectively. In particular, a new optic disc localization method using parabolic fitting is proposed based on the physiological structure characteristics of optic disc and blood vessels. Then, early lesions, such as microaneurysms, hemorrhages and hard exudates, are detected based on their respective characteristics. An equivalent optical model simulating human eyes is designed based on the anatomical structure of retina. Main structures and early lesions are reconstructed in the 3D space for better visualization. Finally, the severity of each image is evaluated based on the international criteria of diabetic retinopathy. RESULTS: The system has been tested on public databases and images from hospitals. Experimental results demonstrate that the proposed system achieves high accuracy for main structures and early lesions detection. The results of severity classification for non-proliferative diabetic retinopathy are also accurate and suitable. CONCLUSIONS: Our system can assist ophthalmologists for clinical diagnosis, automatic screening and course progression of patients. BioMed Central 2017-10-26 /pmc/articles/PMC5659045/ /pubmed/29073912 http://dx.doi.org/10.1186/s12938-017-0414-z Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Xiao, Zhitao
Zhang, Xinpeng
Geng, Lei
Zhang, Fang
Wu, Jun
Tong, Jun
Ogunbona, Philip O.
Shan, Chunyan
Automatic non-proliferative diabetic retinopathy screening system based on color fundus image
title Automatic non-proliferative diabetic retinopathy screening system based on color fundus image
title_full Automatic non-proliferative diabetic retinopathy screening system based on color fundus image
title_fullStr Automatic non-proliferative diabetic retinopathy screening system based on color fundus image
title_full_unstemmed Automatic non-proliferative diabetic retinopathy screening system based on color fundus image
title_short Automatic non-proliferative diabetic retinopathy screening system based on color fundus image
title_sort automatic non-proliferative diabetic retinopathy screening system based on color fundus image
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5659045/
https://www.ncbi.nlm.nih.gov/pubmed/29073912
http://dx.doi.org/10.1186/s12938-017-0414-z
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