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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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Classification and Recognition of Pathologic Foundings in Eye Retina Images
Macek, Ján ; Orság, Filip (referee) ; Drahanský, Martin (advisor)
Diabetic retinopathy and age-related macular degeneration are two of the most common retinal diseases in these days, which can lead to partial or full loss of sight. Due to it, it is necessary to create new approaches enabling to detect these diseases and inform the patient about his condition in advance. The main objective of this work is to design and to implement an algorithm for retinal diseases classification based on images of the patient's retina of previously mentioned diseases. In the first part of this work, there is described in detail each stage of each disease and its the most frequent symptoms. In this thesis, there is also a chapter about fundus camera, which is a tool for image creation of human eye retina. In the second part of this thesis, there is proposed an approach for classification of diabetic retinopathy and age-related macular degeneration. There is also a chapter about algorithmic methods which can be used for image processing and object detection in image. The last part of this thesis contains the test results and their evaluation. Assessment of success of proposed and implemented methods is also part of this chapter.
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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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Comparison of Retinal Images with Pathological Findings
Palacková, Bianca ; Semerád, Lukáš (referee) ; Drahanský, Martin (advisor)
The goal of this thesis is to design and implement software for comparison of retinal images with pathological findings. The most common diseases affecting the retina are diabetic retinopathy and age related macular degeneration. Detection of main components such as optic disc and fovea, need to be detected for proper comparison and detection of diseases. 570 images was used for evaluation of detection of these main components. In both cases, algorithm achieved success over 90\%. 120 images were analysed by 3 ophthalmologists for evaluation of ability to locate pathological findings. Automatic comparison of retinal images can be useful for determination of disease progression.
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Comparison of Retinal Images with Pathological Findings
Palacková, Bianca ; Semerád, Lukáš (referee) ; Drahanský, Martin (advisor)
The goal of this thesis is to design and implement software for comparison of retinal images with pathological findings. The most common diseases affecting the retina are diabetic retinopathy and age related macular degeneration. Detection of main components such as optic disc and fovea, need to be detected for proper comparison and detection of diseases. 570 images was used for evaluation of detection of these main components. In both cases, algorithm achieved success over 90\%. 120 images were analysed by 3 ophthalmologists for evaluation of ability to locate pathological findings. Automatic comparison of retinal images can be useful for determination of disease progression.
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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.
|
|
|
Classification and Recognition of Pathologic Foundings in Eye Retina Images
Macek, Ján ; Orság, Filip (referee) ; Drahanský, Martin (advisor)
Diabetic retinopathy and age-related macular degeneration are two of the most common retinal diseases in these days, which can lead to partial or full loss of sight. Due to it, it is necessary to create new approaches enabling to detect these diseases and inform the patient about his condition in advance. The main objective of this work is to design and to implement an algorithm for retinal diseases classification based on images of the patient's retina of previously mentioned diseases. In the first part of this work, there is described in detail each stage of each disease and its the most frequent symptoms. In this thesis, there is also a chapter about fundus camera, which is a tool for image creation of human eye retina. In the second part of this thesis, there is proposed an approach for classification of diabetic retinopathy and age-related macular degeneration. There is also a chapter about algorithmic methods which can be used for image processing and object detection in image. The last part of this thesis contains the test results and their evaluation. Assessment of success of proposed and implemented methods is also part of this chapter.
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