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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....
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2016
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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. |
format | Online Article Text |
id | pubmed-5072151 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
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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