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Disease Detection in Eye Retina Image
Koštialik, Daniel ; Semerád, Lukáš (referee) ; Maruniak, Lukáš (advisor)
Diabetic retinopathy and age related macular degeneration are among the most common eye retina diseases, which cause partial or complete blindness. The main goal of this thesis is to design and implement software for automatic detection of symptoms from eye fundus images. The detection algorithm is based on the image segmentation by region growing method 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 were analysed and algorithm reaches more than 90 % average success rate. The algorithm, in combination with appropriate hardware and optic mechanism, forms one of practical application in global population screening. Thanks the automatic detection it is possible to determine the presence of symptoms and start an early treatment.
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Detection and Recognition of Diabetes Disease Impacts to the Human Eye Retina
Jausch, Andrej ; Semerád, Lukáš (referee) ; Drahanský, Martin (advisor)
This bachelor's thesis deals with the design of algorithms for the recognition of a diabetes disease impacts to the human eye retina. Diabetic retinopathy is one of the most common diseases aecting the retina and its consequences lead to partial or complete weakness. The basis of the algorithm for detection is to create candidate areas from dierent viewpoints of image processing - computer vision and their subsequent analysis. Core components of the retina have impacts to detection results - optical disc and blood vessels, which need to be properly detected and subsequently excluded from processing. Testing the implemented application took place in 68 images selected from two databases. One of the possible uses of the proposed methods in the future is in combination with the retinal scanning device for the automatic detection of diabetes symptoms during the retinal screening process.
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Detection and Recognition of Diabetes Disease Impacts to the Human Eye Retina
Jausch, Andrej ; Semerád, Lukáš (referee) ; Drahanský, Martin (advisor)
This bachelor's thesis deals with the design of algorithms for the recognition of a diabetes disease impacts to the human eye retina. Diabetic retinopathy is one of the most common diseases aecting the retina and its consequences lead to partial or complete weakness. The basis of the algorithm for detection is to create candidate areas from dierent viewpoints of image processing - computer vision and their subsequent analysis. Core components of the retina have impacts to detection results - optical disc and blood vessels, which need to be properly detected and subsequently excluded from processing. Testing the implemented application took place in 68 images selected from two databases. One of the possible uses of the proposed methods in the future is in combination with the retinal scanning device for the automatic detection of diabetes symptoms during the retinal screening process.
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Disease Detection in Eye Retina Image
Koštialik, Daniel ; Semerád, Lukáš (referee) ; Maruniak, Lukáš (advisor)
Diabetic retinopathy and age related macular degeneration are among the most common eye retina diseases, which cause partial or complete blindness. The main goal of this thesis is to design and implement software for automatic detection of symptoms from eye fundus images. The detection algorithm is based on the image segmentation by region growing method 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 were analysed and algorithm reaches more than 90 % average success rate. The algorithm, in combination with appropriate hardware and optic mechanism, forms one of practical application in global population screening. Thanks the automatic detection it is possible to determine the presence of symptoms and start an early treatment.
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