Conference paper

KOŠTIALIK Daniel, MARUNIAK Lukáš and DRAHANSKÝ Martin. Symptoms Detection in Eye Retina Image. In: 2017 IEEE Symposium Series on Computational Intelligence. Hawaii: IEEE Computer Society, 2017, pp. 3088-3093. ISBN 978-1-5386-4058-6.
Publication language:english
Original title:Symptoms Detection in Eye Retina Image
Title (cs):Detekce symptomů v obrazu sítnice oka
Pages:3088-3093
Proceedings:2017 IEEE Symposium Series on Computational Intelligence
Conference:2017 IEEE Symposium Series on Computational Intelligence
Place:Hawaii, US
Year:2017
ISBN:978-1-5386-4058-6
Publisher:IEEE Computer Society
Keywords
eye retina, symptoma, macula, optic disc, segmentation
Annotation
Diabetic retinopathy and age related macular degeneration are among the most common eye retina diseases, which cause partial or complete blindness. The purpose of this study is to design and implement software for automatic detection of symptoms from eye fundus images. The detection algorithm is based on segmentation methods and follow up analysis of segmented areas. Detection of retina objects such as optic disc, macula and blood vessels is important prior symptoms detection as they can adversely affect the results of the analysis. Total 259 images of four databases were analyzed and algorithm reaches more than 90 % average success rate. The software might be useful in combination with appropriate hardware and optic devices, and can find a practical application in global population screening.
Abstract
Diabetic retinopathy and age related macular degeneration are among the most
common eye retina diseases, which cause partial or complete blindness. The
purpose of this thesis is to design and implement software for automatic
detection of symptoms from eye fundus images. The detection algorithm is based
on segmentation methods and afterwards analysis. Determination of retina objects
such as optic disc, macula and blood vessels is important prior symptoms
detection as they can adversely affect the results of the analysis. Total 259
images of four databases were analyzed and algorithm reaches more than 90 %
average success rate. The software may be useful in combination with appropriate
hardware and optic mechanism, which forms one of practical application in global
population screening.
BibTeX:
@INPROCEEDINGS{
   author = {Daniel Ko{\v{s}}tialik and Luk{\'{a}}{\v{s}}
	Maruniak and Martin Drahansk{\'{y}}},
   title = {Symptoms Detection in Eye Retina Image},
   pages = {3088--3093},
   booktitle = {2017 IEEE Symposium Series on Computational Intelligence},
   year = {2017},
   location = {Hawaii, US},
   publisher = {IEEE Computer Society},
   ISBN = {978-1-5386-4058-6},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php.en.iso-8859-2?id=11531}
}

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