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Classification of arteries and veins in retinal image data
Černohorská, Lucie ; Jakubíček, Roman (referee) ; Kolář, Radim (advisor)
This master's thesis deals with the classification of the retinal blood vessels in retinal image data. The thesis contains a description of anatomy of the human eye with focus on the blood circulation, and imaging and diagnostic methods of the retina are briefly mentioned further. The thesis also summarizes methods of the blood circulation classification with emphasis on the deep learning. The practical section was implemented in Python programming language and describes the pre-processing of the data with determination of AV ratio. Based on a literature search, the U-net architecture was chosen for the classification of the retinal blood vessels. The architecture was modified using the open-source Keras library and tested on images from the experimental video-ophthalmoscope. The modified architecture was initially used for classification of vessels into the corresponding classes and because of unsatisfying results was modified another architecture segmenting retinal vessels, arteries or veins and a proposition of a method of the blood vessels classification.
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Classification of retinal blood vessels
Mitrengová, Jana ; Kolář, Radim (referee) ; Odstrčilík, Jan (advisor)
The thesis deals with the classification of the retinal blood vessels in retinal image data. The first part of the thesis deals with the anatomy of the human eye and focuses on the description of the retina and its blood circulation. It further describes the principle of fundus camera and experimental video ophthalmoscope. The second part of the thesis is devoted to a literature search of academic publications that deal with the classification of the retinal vessels into arteries and veins. Subsequently, the principle of selected machine learning methods is presented. Based on the literature research, two methods for the classification of the blood vessels were proposed, the first one using the SVM classifier and the second one using the convolutional neural network U-Net. At the end, the analysis of vascular pulsations was performed. The practical part of the thesis was carried out in Matlab programming interface and images from the RITE, IOSTAR and AFIO database were used for classification and the retinal video sequences taken with an experimental video ophthalmoscope were processed in the analysis of pulsations.
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Detection of blood-vessel bifurcations in retina
Baše, Michal ; Zoltán, Szabó (referee) ; Kolář, Radim (advisor)
This master thesis deals with detection of blood-vessel bifurcations in retinal images and its properties. There are explained procedure of taking photographs of retina by fundus camera, optical coherence tomography (OCT) and scanning laser opthalmoscope (SLO) and properties of fundus images are described. In this thesis are mentioned some effective thresholding methods and there are explained the most important morphological operations with binary images, as well as with grayscale images. Detected bifurcations are used for image registration with second-order polynomial transformation using corresponding bifurcations.
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Classification of retinal blood vessels
Mitrengová, Jana ; Kolář, Radim (referee) ; Odstrčilík, Jan (advisor)
The thesis deals with the classification of the retinal blood vessels in retinal image data. The first part of the thesis deals with the anatomy of the human eye and focuses on the description of the retina and its blood circulation. It further describes the principle of fundus camera and experimental video ophthalmoscope. The second part of the thesis is devoted to a literature search of academic publications that deal with the classification of the retinal vessels into arteries and veins. Subsequently, the principle of selected machine learning methods is presented. Based on the literature research, two methods for the classification of the blood vessels were proposed, the first one using the SVM classifier and the second one using the convolutional neural network U-Net. At the end, the analysis of vascular pulsations was performed. The practical part of the thesis was carried out in Matlab programming interface and images from the RITE, IOSTAR and AFIO database were used for classification and the retinal video sequences taken with an experimental video ophthalmoscope were processed in the analysis of pulsations.
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Classification of arteries and veins in retinal image data
Černohorská, Lucie ; Jakubíček, Roman (referee) ; Kolář, Radim (advisor)
This master's thesis deals with the classification of the retinal blood vessels in retinal image data. The thesis contains a description of anatomy of the human eye with focus on the blood circulation, and imaging and diagnostic methods of the retina are briefly mentioned further. The thesis also summarizes methods of the blood circulation classification with emphasis on the deep learning. The practical section was implemented in Python programming language and describes the pre-processing of the data with determination of AV ratio. Based on a literature search, the U-net architecture was chosen for the classification of the retinal blood vessels. The architecture was modified using the open-source Keras library and tested on images from the experimental video-ophthalmoscope. The modified architecture was initially used for classification of vessels into the corresponding classes and because of unsatisfying results was modified another architecture segmenting retinal vessels, arteries or veins and a proposition of a method of the blood vessels classification.
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Detection of blood-vessel bifurcations in retina
Baše, Michal ; Zoltán, Szabó (referee) ; Kolář, Radim (advisor)
This master thesis deals with detection of blood-vessel bifurcations in retinal images and its properties. There are explained procedure of taking photographs of retina by fundus camera, optical coherence tomography (OCT) and scanning laser opthalmoscope (SLO) and properties of fundus images are described. In this thesis are mentioned some effective thresholding methods and there are explained the most important morphological operations with binary images, as well as with grayscale images. Detected bifurcations are used for image registration with second-order polynomial transformation using corresponding bifurcations.
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