Automated Quality Assessment of Fundus Images via Analysis of Illumination, Naturalness and Structure

In remote medical diagnosis, the percentage of poor-quality fundus images is very high, which requires automated quality assessment of fundus images in the acquisition stage to reduce the retransmission cost. In this paper, we propose a fundus image quality classifier via the analysis of illuminatio...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Feng Shao [verfasserIn] Yan Yang [verfasserIn] Qiuping Jiang [verfasserIn] Gangyi Jiang [verfasserIn] Yo-Sung Ho [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Fundus image quality assessment illumination level naturalness level structure level

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 6(2018), Seite 806-817
Übergeordnetes Werk:	volume:6 ; year:2018 ; pages:806-817

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2017.2776126

Katalog-ID:	DOAJ068466080

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allfieldsSound	10.1109/ACCESS.2017.2776126 doi (DE-627)DOAJ068466080 (DE-599)DOAJ67c851d1ed1b432dbe961d193e1d12ec DE-627 ger DE-627 rakwb eng TK1-9971 Feng Shao verfasserin aut Automated Quality Assessment of Fundus Images via Analysis of Illumination, Naturalness and Structure 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In remote medical diagnosis, the percentage of poor-quality fundus images is very high, which requires automated quality assessment of fundus images in the acquisition stage to reduce the retransmission cost. In this paper, we propose a fundus image quality classifier via the analysis of illumination, naturalness, and structure, which use three effective secondary indices (or 5-D feature set) and different classification methods to determine the recommendation indexes of fundus images for further diagnosis. We construct a fundus image database including `accept' and `reject' classes based on the definition of illumination, naturalness, and structure. The model can achieve a sensitivity of 94.69%, specificity of 92.29%, and accuracy of 93.60% for the classifying of the fundus images. Fundus image quality assessment illumination level naturalness level structure level Electrical engineering. Electronics. Nuclear engineering Yan Yang verfasserin aut Qiuping Jiang verfasserin aut Gangyi Jiang verfasserin aut Yo-Sung Ho verfasserin aut In IEEE Access IEEE, 2014 6(2018), Seite 806-817 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:6 year:2018 pages:806-817 https://doi.org/10.1109/ACCESS.2017.2776126 kostenfrei https://doaj.org/article/67c851d1ed1b432dbe961d193e1d12ec kostenfrei https://ieeexplore.ieee.org/document/8116608/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 806-817
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source	In IEEE Access 6(2018), Seite 806-817 volume:6 year:2018 pages:806-817
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topic_facet	Fundus image quality assessment illumination level naturalness level structure level Electrical engineering. Electronics. Nuclear engineering
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authorswithroles_txt_mv	Feng Shao @@aut@@ Yan Yang @@aut@@ Qiuping Jiang @@aut@@ Gangyi Jiang @@aut@@ Yo-Sung Ho @@aut@@
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callnumber-first	T - Technology
author	Feng Shao
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topic_title	TK1-9971 Automated Quality Assessment of Fundus Images via Analysis of Illumination, Naturalness and Structure Fundus image quality assessment illumination level naturalness level structure level
topic	misc TK1-9971 misc Fundus image misc quality assessment misc illumination level misc naturalness level misc structure level misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Fundus image misc quality assessment misc illumination level misc naturalness level misc structure level misc Electrical engineering. Electronics. Nuclear engineering
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title	Automated Quality Assessment of Fundus Images via Analysis of Illumination, Naturalness and Structure
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title_sort	automated quality assessment of fundus images via analysis of illumination, naturalness and structure
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title_auth	Automated Quality Assessment of Fundus Images via Analysis of Illumination, Naturalness and Structure
abstract	In remote medical diagnosis, the percentage of poor-quality fundus images is very high, which requires automated quality assessment of fundus images in the acquisition stage to reduce the retransmission cost. In this paper, we propose a fundus image quality classifier via the analysis of illumination, naturalness, and structure, which use three effective secondary indices (or 5-D feature set) and different classification methods to determine the recommendation indexes of fundus images for further diagnosis. We construct a fundus image database including `accept' and `reject' classes based on the definition of illumination, naturalness, and structure. The model can achieve a sensitivity of 94.69%, specificity of 92.29%, and accuracy of 93.60% for the classifying of the fundus images.
abstractGer	In remote medical diagnosis, the percentage of poor-quality fundus images is very high, which requires automated quality assessment of fundus images in the acquisition stage to reduce the retransmission cost. In this paper, we propose a fundus image quality classifier via the analysis of illumination, naturalness, and structure, which use three effective secondary indices (or 5-D feature set) and different classification methods to determine the recommendation indexes of fundus images for further diagnosis. We construct a fundus image database including `accept' and `reject' classes based on the definition of illumination, naturalness, and structure. The model can achieve a sensitivity of 94.69%, specificity of 92.29%, and accuracy of 93.60% for the classifying of the fundus images.
abstract_unstemmed	In remote medical diagnosis, the percentage of poor-quality fundus images is very high, which requires automated quality assessment of fundus images in the acquisition stage to reduce the retransmission cost. In this paper, we propose a fundus image quality classifier via the analysis of illumination, naturalness, and structure, which use three effective secondary indices (or 5-D feature set) and different classification methods to determine the recommendation indexes of fundus images for further diagnosis. We construct a fundus image database including `accept' and `reject' classes based on the definition of illumination, naturalness, and structure. The model can achieve a sensitivity of 94.69%, specificity of 92.29%, and accuracy of 93.60% for the classifying of the fundus images.
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title_short	Automated Quality Assessment of Fundus Images via Analysis of Illumination, Naturalness and Structure
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