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Detection of Diseases Caused by Diabetes in Retinal Images
Zapletal, Michal ; Semerád, Lukáš (referee) ; Kavetskyi, Andrii (advisor)
The goal of this thesis is to design and implement an algorithm for detecting exudates and microaneurysms in colored retinal images. These diseases are the first signs of diabetic retinopathy and early detection is crucial. The proposed algorithm begins with preprocessing, where excess background is removed, contrast is enhanced using CLAHE and histogram stretching, and noise filtering is applied. Optic disc localization is based on iterative background removal and row and column variances. Exudates detection is performed based on gamma correction, thresholding and optic disc removal. Microaneurysm detection is based on morphological operations, hit-or-miss transformation and principal component analysis (PCA). The algorithm was tested on 4 datasets with accuracy 73,1 % for exudates and 73,3 % for microaneurysms. The resulting program could assist in automatic disease detection, which could potentially save time for doctors.
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Pathologies Detection in Retinal Images
Hurta, David ; Drahanský, Martin (referee) ; Kavetskyi, Andrii (advisor)
The main goal of this work is to design and implement an algorithm for the detection of microaneurysms, hard exudates, and soft exudates on color fundus images. An algorithm for detecting objects based on deep learning has been proposed. The Faster R-CNN architecture with a feature pyramid network and a pre-pretrained residual network was used together with various data transformation methods. A total of six retinal image datasets were used to train, validate and test the models. The trained models achieved 0.46 mean average accuracy (mAP) in microaneurysm detection and 0.48 mAP in exudates detection during testing. The resulting models have been compared with published articles and make it possible to detect given pathologies with commendable accuracy.
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Hard and soft exudates detection in retinal images
Válková, Hana ; Lamoš, Martin (referee) ; Kolář, Radim (advisor)
The thesis deals with automatic detection of soft and hard exudates in retinal images of the human eye. In its introduction the thesis describes the issue of diabetes in relation to the damage to the retina of the eye. What is described in the first place is diabetic retinopathy, its symptoms and progression of the disease. Another section is devoted to describing DIARETDB1, the freely accessible database which besides other things contains a set of images showing various degrees of disease, evaluation of images from the experts and the evaluation protocol. The next section discusses several methods for automatic detection of hard and soft exudates. The practical part of the bachelor’s thesis is aimed at image pre-processing with respect to the normalization of retinal images, the selected method for adaptive transformation of contrast was implemented. This part also containts description of chosen methology of thresholding, feature extraction based on lesions intensity and its surroundings, use of Ho Kashyap classifier is described, classification of lesions in images is followed. In conclusion realized methods is evaluated.
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Pathologies Detection in Retinal Images
Hurta, David ; Drahanský, Martin (referee) ; Kavetskyi, Andrii (advisor)
The main goal of this work is to design and implement an algorithm for the detection of microaneurysms, hard exudates, and soft exudates on color fundus images. An algorithm for detecting objects based on deep learning has been proposed. The Faster R-CNN architecture with a feature pyramid network and a pre-pretrained residual network was used together with various data transformation methods. A total of six retinal image datasets were used to train, validate and test the models. The trained models achieved 0.46 mean average accuracy (mAP) in microaneurysm detection and 0.48 mAP in exudates detection during testing. The resulting models have been compared with published articles and make it possible to detect given pathologies with commendable accuracy.
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Hard and soft exudates detection in retinal images
Válková, Hana ; Lamoš, Martin (referee) ; Kolář, Radim (advisor)
The thesis deals with automatic detection of soft and hard exudates in retinal images of the human eye. In its introduction the thesis describes the issue of diabetes in relation to the damage to the retina of the eye. What is described in the first place is diabetic retinopathy, its symptoms and progression of the disease. Another section is devoted to describing DIARETDB1, the freely accessible database which besides other things contains a set of images showing various degrees of disease, evaluation of images from the experts and the evaluation protocol. The next section discusses several methods for automatic detection of hard and soft exudates. The practical part of the bachelor’s thesis is aimed at image pre-processing with respect to the normalization of retinal images, the selected method for adaptive transformation of contrast was implemented. This part also containts description of chosen methology of thresholding, feature extraction based on lesions intensity and its surroundings, use of Ho Kashyap classifier is described, classification of lesions in images is followed. In conclusion realized methods is evaluated.
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