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A goal in human embryonic stem cell (hESC) research is the faithful differentiation to given cell types such as neural lineages. During embryonic development, a basement membrane surrounds the neural plate that forms a tight, apico-basolaterally polarized epithelium before closing to form a neural tube with a single lumen. Here we show that the three-dimensional epithelial cyst culture of hESCs in Matrigel combined with neural induction results in a quantitative conversion into neuroepithelial cysts containing a single lumen. Cells attain a defined neuroepithelial identity by 5 days. The neuroepithelial cysts naturally generate retinal epithelium, in part due to IGF-1/insulin signaling. We demonstrate the utility of this epithelial culture approach by achieving a quantitative production of retinal pigment epithelial (RPE) cells from hESCs within 30 days. Direct transplantation of this RPE into a rat model of retinal degeneration without any selection or expansion of the cells results in the formation of a donor-derived RPE monolayer that rescues photoreceptor cells. The cyst method for neuroepithelial differentiation of pluripotent stem cells is not only of importance for RPE generation but will also be relevant to the production of other neuronal cell types and for reconstituting complex patterning events from three-dimensional neuroepithelia.

This paper deals with identification of persons using retinal biometry. The retinal vasculature is invariant and unique to everyone, which determines it for biometric purposes. The first part of the work includes information about biometrics, biometric systems and reliability measures. The next part describes the principle of using experimental video ophthalmoscope, which was used for retinal vascular imaging and includes the literature research of use of retinal images for biometrics, feature extraction methods and similarity measures. Finally, two algorithms to use the input data are proposed and realized in programming environment MATLAB®. The methods are tested and evaluated on a data set from experimental video ophthalmoscope and on publicly available STRaDe and DRIVE databases.

The aim of the following thesis was to study the issue of optical disc and retinal vessels segmentation in ophthalmologic sequences. The theoretical part of the thesis summarizes the principles of different approaches in the field of deep learning, which are used in connection with the given issue. Based on the theoretical part, methods for optical disk segmentation and retinal vessel segmentation based on the convolutional neural networks Linknet, PSPNet, Unet and MaskRCNN are proposed. The practical part of the thesis deals with the description of their implementation and subsequent evaluation.

Diplomová práca sa zaoberá detekciou a hodnotením skreslených snímok v retinálnych obrazových dátach. Teoretická časť obsahuje stručné zhrnutie anatómie oka a metód hodnotenia kvality obrazov všeobecne, ako aj konkrétne hodnotenie retinálnych obrazov. Praktická časť bola vypracovaná v programovacom jazyku Python. Obsahuje predspracovanie dostupných retinálnych obrazov za účelom vytvorenia vhodného datasetu. Ďalej je navrhnutá metóda hodnotenia troch typov šumu v skreslených retinálnych obrazoch, presnejšie pomocou Inception-ResNet-v2 modelu. Táto metóda nebola prijateľná a navrhnutá bola teda iná metóda pozostávajúca z dvoch krokov - klasifikácie typu šumu a následného hodnotenia úrovne daného šumu. Pre klasifikáciu typu šumu bolo využité filtrované Fourierove spektrum a na hodnotenie obrazu boli využité príznaky extrahované pomocou ResNet50, ktoré vstupovali do regresného modelu. Táto metóda bola ďalej rozšírená ešte o krok detekcie zašumených snímok v retinálnych sekvenciách.

This thesis attempts to find an alternative method for biometric identification using retinal images. First part is focused on the introduction to biometrics, human eye anatomy and methods used for retinal biometry. The essence of neural networks and deep learning methods is described as it will be used practically. In the last part of the thesis a chosen identification algorithm and its implementation is described and the results are presented.

The autofluorescence images of the retina, which are obtained by laser scanning confocal ophthalmoscope Heidelberg Retina Angiograph (HRA), are used for early glaucoma diagnosis. Glaucoma is caused by gradual damage of the optic nerves, which leads to the failing of eyesight and can even cause its complete loss. HRA system is used to obtain a set of retinal images, where every image represents certain segment of retina. This thesis describes image registration methods for retinal image mosaicing. The tested registration methods are based on the global function of similarity or the landmarks detection. As landmarks, the blood vessel bifurcations are considered.

This thesis is deals with the nerve fibre layer in the colour ophthalmology images of retina. The thesis describes how can we use finding of nerve fibre layer and how was it solved in the past. In the thesis are proposed the methods that are based on processing and scoring frequency spectrums of individual sample of retina. At first here are described the methods of detection on the artificial generated samples that just simulate the nerve fibre layer. Then the thesis concentrates on processing of real images of retina. Because of the bloodstream, that depreciates processing at real images, are all surveyed samples are chosen manually. Except detection the nerve fibre layer itself, the thesis also deals with determination of direction their dissemination.

This work deals with mathematical description of nerve fiber bundle trajectories for the diagnosis of glaucoma. Also gives a brief explanation in the principle of fundus camera and glaucoma. Its aim is to implement the model into MATLAB software, to project a~methodics of its use and also to realize it.

The main topic of the thesis is creation of formulas for the amount of information entropy in biometric characteristics of iris and retina. This field of science in biometrics named above is unstudied yet, so the thesis tries to initiate research in this direction. The thesis also discusses the historical context of security and identification fields according to biometric characteristics of a human being with an overlap for potential usage in biometrics of iris and retina. The Daugman’s algorithm for converting iris image into the binary code which can be used as a cryptographic key is discussed in detail. An application implementing this conversion is also a part of the thesis.