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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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Semantic Segmentation of Pathologies in Retinal Images
Čabala, Roman ; Orság, Filip (referee) ; Kavetskyi, Andrii (advisor)
The thesis aimed to segment pathology visible in the retina images, such as exudates, hemorrhages, and microaneurysms. For that, two well known deep neural networks, named U-Net and SegFormer, were trained. To test the performance of the models, one publicly available dataset was used, named IDRiD. Obtained results were reported after analyzing different factors which affected the performance of the models U-Net and Segformer.
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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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Automatic detection of microaneurysms in fundus images
Klímová, Markéta ; Walek, Petr (referee) ; Kolář, Radim (advisor)
Diabetic retinopathy is a serious complication of diabetes mellitus. It develops as a result of total damage of vessels caused by hyperglycemia and it is one of major causes of blindness. The microaneurysms are the first clinically observed pathologies of diabetic retinopathy. The aim of this bachelor thesis is to propose and to implement an automated microaneurysm detector. Teoretical part describes the eye anatomy, diabetic retinopathy and some existing methods of automated detection. Next the implemented solution is described and the results of the detectin are evaluated.
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Microaneurysms and hemorrhages detection in retinal images
Tobiášová, Nela ; Štohanzlová, Petra (referee) ; Kolář, Radim (advisor)
Diabetic retinopathy is a serious eye complication of diabetes mellitus and one of the major causes of blindness in the world. The microaneurysms and the haemorrhages are the pathologies of diabetic retinopathy. Their detection can halt or reverse the progression of this disease and prevent blindness. The algorithms could be helpful to ophthalmologists. This bacherol’s thesis is concerned with the detection of microaneurysms and haemorrhages in fundus images. The diabetic retinopathy, the types of lesions and the treatment methods are described in the first part of the paper. Existing methods are described as follows. The practical part of this work is aimed at the proposal and the detection of the red lesions. It consists of several steps, such as selecting the correct channel of RGB images, using local methods of contrast enhancement, edge detection, thresholding, creating a training set of the feature vector and the classification with the use of the neutral network.
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Tool for Detection and Correction of Images with Diseased Eye Retinas
Jochlík, Jakub ; Semerád, Lukáš (referee) ; Drahanský, Martin (advisor)
Loss or partial loss of eye sight can have major effect on quality of person's life. One of the most common diseases, which causes loss or partial loss of eye sight are diabetic retinopathy and age releated macular degeneration. Both of these diseases can be prevented or mediated by early detection and proper treatment. The fundus camera, which is used to capture eye retina, has had major effect on increasing quality and speed of early detection. Images captured by fundus camera can be automatically analyzed in order to detect any possible signs of retina damage. This thesis proposes one possible way of automating this process. First part of this thesis describes eye, its diseases and capturing technology. Second part then proposes way of automating detection process and its implementation. Lastly, the results are evaluated.
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Semantic Segmentation of Pathologies in Retinal Images
Čabala, Roman ; Orság, Filip (referee) ; Kavetskyi, Andrii (advisor)
The thesis aimed to segment pathology visible in the retina images, such as exudates, hemorrhages, and microaneurysms. For that, two well known deep neural networks, named U-Net and SegFormer, were trained. To test the performance of the models, one publicly available dataset was used, named IDRiD. Obtained results were reported after analyzing different factors which affected the performance of the models U-Net and Segformer.
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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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Tool for Detection and Correction of Images with Diseased Eye Retinas
Jochlík, Jakub ; Semerád, Lukáš (referee) ; Drahanský, Martin (advisor)
Loss or partial loss of eye sight can have major effect on quality of person's life. One of the most common diseases, which causes loss or partial loss of eye sight are diabetic retinopathy and age releated macular degeneration. Both of these diseases can be prevented or mediated by early detection and proper treatment. The fundus camera, which is used to capture eye retina, has had major effect on increasing quality and speed of early detection. Images captured by fundus camera can be automatically analyzed in order to detect any possible signs of retina damage. This thesis proposes one possible way of automating this process. First part of this thesis describes eye, its diseases and capturing technology. Second part then proposes way of automating detection process and its implementation. Lastly, the results are evaluated.
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Automatic detection of microaneurysms in fundus images
Klímová, Markéta ; Walek, Petr (referee) ; Kolář, Radim (advisor)
Diabetic retinopathy is a serious complication of diabetes mellitus. It develops as a result of total damage of vessels caused by hyperglycemia and it is one of major causes of blindness. The microaneurysms are the first clinically observed pathologies of diabetic retinopathy. The aim of this bachelor thesis is to propose and to implement an automated microaneurysm detector. Teoretical part describes the eye anatomy, diabetic retinopathy and some existing methods of automated detection. Next the implemented solution is described and the results of the detectin are evaluated.
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