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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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Calculation of advanced diffusion parameters in brain grey matter from DKI MRI images
Pánková, Olga ; Jakubíček, Roman (referee) ; Minsterová, Alžběta (advisor)
Thesis named Calculation of advanced diffusion parameters in brain grey matter from DKI MRI images deals with processing of diffusion-weighted images from DKI. The thesis contains review of literature on principle of diffusion, influence of diffusion on MRI, calculation of DTI and DKI parameters and clinical application of diffusion-weighted maps with focus on grey matter. The thesis focuses on software tools for processing and pre-processing DTI and DKI. The practical part consisted of two sections. Two different softwares were used to calculate maps of diffusion parameters. Diffusion parameters from anatomical structure sunstantia nigra were compared between group of healthy controls and patients with Parkinson’s disease. This comparison did not show any statisticaly significant difference. In the second step, a script for creating diffusion maps in software Diffusinal Kurtosis Estimator was made.
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Reduction of metal artifacts in CT data with submicron resolution
Víteček, Jiří ; Mézl, Martin (referee) ; Jakubíček, Roman (advisor)
This diploma thesis deals with reduction of metal artifacts in CT data with submicron resolution. The first part of this thesis briefly describes x-ray computed tomography followed by the description of artifacts of tomographic images and existing approaches of the reduction of metal artifacts. In the second part proposed methods of reduction of metal artifacts and their implementation in Matlab programming environment are described. Finally functionality of algorithms is tested on a newly created database and the results are compared, evaluated and discussed.
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Tissue characterisation in spectral CT data
Poláková, Veronika ; Jan, Jiří (referee) ; Jakubíček, Roman (advisor)
This bachelor thesis deals with tissue characterisation in virtual monoenergetic images (VMI). Firstly, literature survey presents spectral CT which allows reconstructing VMI. Secondly, statistical evaluation of tissue CT numbers was made for all energies of VMI which were reconstructed. It was found that with growing energy of VMI CT number increases or decreases with different steepness depending on a type of tissue. As a consequence, the suitable VMI offer better contrast resolution between selected pairs of tissues, which enables better tissue segmentation and classification in these images.
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3D reconstruction from multiple images
Řehoř, Jan ; Odstrčilík, Jan (referee) ; Jakubíček, Roman (advisor)
This thesis deals with 3D reconstruction of objects from multiple frames. Thesis is divided into five chapters. The first one is connected with theory, so, it is about 3D reconstruction, about camera geometry description and about reconstruction algorithms. The following are made up by practical parts, in which are described single steps of 3D reconstruction realization, so, it is about frame acquisition, camera calibration and about used algorithms. Last part of the thesis is made up by discussion and conclusion in which the results of the experiment are discussed and summarized.
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Detection and segmentation of lumbar vertebrae in 3D CT data
Nemček, Jakub ; Kolář, Radim (referee) ; Jakubíček, Roman (advisor)
This thesis deals with the detection and the segmentation of lumbar vertebrae in CT image datas. The described detection method is based on the use of a trained SVM classificator and histograms of oriented gradients as the image features. The detection method is applied on two-dimensional sagital slices of the CT image. The segmentation method is implemented as triangular mesh model deformation of models, that are obtained from averaged vertebrae in real CT datas. The first part of the thesis describes essential theoretical knowledge about the anatomy of the axial skeleton, computer tomography, image processing methods and about the detection and segmentation issues. The second part contains the algorithms realisation description, the evaluation and the discussion of the results. Applications of the algorithms in CAD systems is described at the end. The application of all of the points is done in the programming software Matlab.
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Segmentation of the cord canal and intervertebral discs in MRI data
Koban, Martin ; Odstrčilík, Jan (referee) ; Jakubíček, Roman (advisor)
The concern of this thesis is development of the method for the spinal canal and intervertebral discs segmentation in volume MRI data. The primary aim is to achieve the highest possible level of automation and accuracy allowing for reliable quantitative evaluation of the results. The algorithm is based on the random walk model in combination with a specific active contour method formulated through level set concept. The proposed approach is tested using a database of three-dimensional T2-weighted MR images, which also contains referential manual segmentation of intervertebral discs.
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Convolutional neural networks for identification of axial 2D slices in CT data
Vavřinová, Pavlína ; Harabiš, Vratislav (referee) ; Jakubíček, Roman (advisor)
This thesis deals with the classification of axial 2D slices in CT patient’s data into six categories. The sphere of convolutional neural networks was used for this purpose. For a better understanding of this issue, the basics of neural networks and then the principles of deep learning including convolutional neural networks are explained at first. The AlexNet network was specifically selected for the intention of this identification, and it was tested on the created data set after being adaptated. The overall classification success rate was 86% ,after the final adjustments, a slight improvement was achieved and the identification success rate was 87%.
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Evaluation of eye-blinking artifact effect on fusion result of simultaneous EEG-fMRI data
Dobiš, Lukáš ; Jakubíček, Roman (referee) ; Labounek, René (advisor)
This thesis sets a theoretical framework about simultaneous EEG-fMRI fusion. The work contains a description of basic principles of acquisition, their individual artifact types and preprocessing techniques for each type of data. Thesis mainly deals with suppression of eye blink artifacts in EEG data, by the method of independent component analysis. The following part explains the technique of simultaneous EEG-fMRI fusion in a general linear model and the creation of activation maps of statistically important correlations. This chapter is concluded with a description of methodology needed for result analysis. Finally, the used data are described, and a solution is proposed and applied in process of EEG preprocessing with artifact suppression, data fusion and result evaluation in MATLAB environment. Evaulation results showed that eye blink artifact influences the fusion result computed from relative power values more then that constructed via absolute power values. Tested method didnt supress eye blink artifact completely.
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Head pose estimation via stereoscopic reconstruction
Hříbková, Veronika ; Jakubíček, Roman (referee) ; Kolář, Radim (advisor)
The thesis deals with head pose estimation in stereo data. The theoretical part provides the basis for understanding the geometry of the camera, its parameters and the method of calibration. The following describes the principles of stereo analysis and creating of disparity maps. In the research section, the methods used for head pose modelling are presented and an analysis of selected published articles is given. In the course of the master’s thesis, a system of two cameras for stereoscopic acquisition of motion of the head was designed and several measurements were carried out. The obtained data was prepared for creation of disparity maps and further processing. Based on the detection of facial features, in particular the inner and outer corners of the eyes and corners of the mouth, and their correspondences, a simple geometric model in shape of triangle was created to illustrate the inclination of the facial plane in space. By computing the angle of inclination in three axes, the current head pose is obtained. Motion is modelled by tracking detected points during video sequences.
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