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A review on automatic analysis techniques for color fundus photographs

In this paper, we give a review on automatic image processing tools to recognize diseases causing specific distortions in the human retina. After a brief summary of the biology of the retina, we give an overview of the types of lesions that may appear as biomarkers of both eye and non-eye diseases....

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Detalles Bibliográficos
Autores principales: Besenczi, Renátó, Tóth, János, Hajdu, András
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5072151/
https://www.ncbi.nlm.nih.gov/pubmed/27800125
http://dx.doi.org/10.1016/j.csbj.2016.10.001
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author Besenczi, Renátó
Tóth, János
Hajdu, András
author_facet Besenczi, Renátó
Tóth, János
Hajdu, András
author_sort Besenczi, Renátó
collection PubMed
description In this paper, we give a review on automatic image processing tools to recognize diseases causing specific distortions in the human retina. After a brief summary of the biology of the retina, we give an overview of the types of lesions that may appear as biomarkers of both eye and non-eye diseases. We present several state-of-the-art procedures to extract the anatomic components and lesions in color fundus photographs and decision support methods to help clinical diagnosis. We list publicly available databases and appropriate measurement techniques to compare quantitatively the performance of these approaches. Furthermore, we discuss on how the performance of image processing-based systems can be improved by fusing the output of individual detector algorithms. Retinal image analysis using mobile phones is also addressed as an expected future trend in this field.
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spelling pubmed-50721512016-10-31 A review on automatic analysis techniques for color fundus photographs Besenczi, Renátó Tóth, János Hajdu, András Comput Struct Biotechnol J Short Survey In this paper, we give a review on automatic image processing tools to recognize diseases causing specific distortions in the human retina. After a brief summary of the biology of the retina, we give an overview of the types of lesions that may appear as biomarkers of both eye and non-eye diseases. We present several state-of-the-art procedures to extract the anatomic components and lesions in color fundus photographs and decision support methods to help clinical diagnosis. We list publicly available databases and appropriate measurement techniques to compare quantitatively the performance of these approaches. Furthermore, we discuss on how the performance of image processing-based systems can be improved by fusing the output of individual detector algorithms. Retinal image analysis using mobile phones is also addressed as an expected future trend in this field. Research Network of Computational and Structural Biotechnology 2016-10-06 /pmc/articles/PMC5072151/ /pubmed/27800125 http://dx.doi.org/10.1016/j.csbj.2016.10.001 Text en © 2016 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Short Survey
Besenczi, Renátó
Tóth, János
Hajdu, András
A review on automatic analysis techniques for color fundus photographs
title A review on automatic analysis techniques for color fundus photographs
title_full A review on automatic analysis techniques for color fundus photographs
title_fullStr A review on automatic analysis techniques for color fundus photographs
title_full_unstemmed A review on automatic analysis techniques for color fundus photographs
title_short A review on automatic analysis techniques for color fundus photographs
title_sort review on automatic analysis techniques for color fundus photographs
topic Short Survey
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5072151/
https://www.ncbi.nlm.nih.gov/pubmed/27800125
http://dx.doi.org/10.1016/j.csbj.2016.10.001
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