Work deals with methods of calibration of multi-center study of magnetic resonance imaging. Magnetic resonance is the phenomenon of the substance in a magnetic field of induction B0 delivers energy in the form of RF pulse with the Larmor frequency and thus to excite particles to higher energy levels. Calibration can be performed using the distinctive and homogeneous RF phantoms. Furthermore, we can perform calibration using image registration. To calibrate the images by registering the work described in the classical linear (affine) and nonlinear. Listed below are the simulators, which are also useful for modeling various artifacts.

The aim of the thesis is optimization for a medical images registration. The basis is to acquaint with the images registration and to peruse component global optimization methods, especially an optimization method TRUST. After theoretic knowledge it is possible to proceed to a suggestion of an optimization method based on the TRUST method and to realize in the program setting MATLAB. Created algorithms has been tested on test data and compared with other optimization methods as Simulated annealing.

The result of this thesis, focused on medical image registration, is an automatic image registration algorithm. It is constructed to be used on real ultrasonography images, created by perfusion imaging. In its introductory part, the thesis deals with registrations methods, next it describes types of optimization principles and single criteria functions served to determine correct image transformation. Based on the theoretical part, there are realized three optimization algorithms using three criterial functions, which served to registration of provided ultrasonography sequences. These algorithms are tested and results are passed on analysis, on its ground are judged its advantages and disadvantages.

This thesis deals with a nonrigid image registration for movement correction in thoracic dynamic contrast CT data. The deformation field is initialized by the analysis of disparities based on nonlinear matched filter, which defines local movement deformation. The values of control points are optimized by the Nelder-Mead method. The transformation model is based on a 4D (3D + time) free-form B-spline deformation for feature of movement distortion. The first part of the thesis briefly discusses the theory of image registration. Knowledge of this theory is necessary for understanding the remaining chapters, which describe the proposed method and its realization. The large part of this thesis is devoted to the geometrical image transformations, that is very important for the image registration. The thesis also describes a simplex method for function minimization. Three publicated methods of registration of medical 4D CT data are given. In the following chapter are individual parts of the purposed nonrigid registration including possible problems and their solution described.

This thesis deal with various methods of background detection and with it related motion detection in a scene. It's progressing from simplest methods to more comlex. For every one are reviewed the possibilities of using and her drawbacks. In introduction are described various types of scenes according to background and foreground type e.g . according to movement objects speed or presence of movement in background. Is proposed several common or specific improvements for obtaining better background even by using simple method. Next part of work solve real situation of shaking camera. There are tested two basic methods for optical stabilization. The first is registration of images by template matching. Alternative method used interest points (corners). Both methods are closely examinate and is sought best way to match following pictures. Except shaking of camera this work deal with rotating camera and in theory solve detection background from cameras placed on ridden car. Part of work is creation database of different types scenes

This semestral thesis deals with visualization of intracranial EEG. In the first part, theoretical basics of EEG is mentioned. After that, image registration, as a needed tool for visualization is described followed by research of methods of visualization of high frequency oscilations from intracranial EEG. Finally, method for visualization of high frequency oscilations from EEG in real MRI patient scans is designed and implemented.

In August, 2014 three experiments were conducted using infrared systems deployed at White Mountain Research center, CA. The data was acquired for the whole month of August. Teams of 3-4 students from Cal Poly San Luis Obispo and UC Santa Barbara were stationed at the research center for 2-3 days to operate the equipment. The three experiments were:(1) creating spatial-temporal time series of lunar surface temperatures;(2) identifying atmospheric meteor trails;(3) search for meteor impacts on the Moon surface. Out of the three this thesis focusses on experiment 1 and the results from this experiment could also help with the other experiments. We propose to use a thermal infrared camera mounted on a telescope to acquire time-series observations of lunar surface temperatures to get a novel insight into the thermal evolution of the Moon over a complete lunar cycle. Half a lunar cycle would account from morning to night and lasts for approximately 14.75 days. Seeing how the pixel value changes from morning to night the pixel and temperature trends can be observed throughout the day. Apart from that one can get the two temperature peaks that could maybe help to get an estimate for the Thermal inertia of the surface in the presence of Moon regolith. The temperature trends and the thermal inertia could potentially provide some insight for methods that seek to determine the properties of asteroids from ground based observations.

Mastitis is a common disease among cows in dairy farms. Diagnosis of the infection is today done manually, by analyzing bacteria growth on agar plates. However, classifiers are being developed for automated diagnostics using images of agar plates. Input images need to be of reasonable quality and consistent in terms of scale, positioning, perspective, and rotation for accurate classification. Therefore, this thesis investigates if a combination of image processing techniques can be used to match each input image to a pre-defined reference model. A method was proposed to identify important key points needed to register the input image to the reference model. The key points were defined by identifying the agar plate, its compartments, and its rotation within the image. The results showed that image registration with the correct key points was sufficient enough to match images of agar plates to a reference model despite any varieties in scale, position, perspective, or rotation. However, the accuracy depended on the identification of the salient features of the agar plate. Ultimately, the work proposes an approach using image registration to transform images of agar plates based on a pre-defined reference model, rather than a reference image.

Every year, the number of applications relying on information extracted from high-resolution satellite imagery increases. In particular, the combined use of different data sources is rising steadily, for example to create high-resolution maps, to detect changes over time or to conduct image classification. In order to correctly fuse information from multiple data sources, the utilized images have to be precisely geometrically registered and have to exhibit a high absolute geo-localization accuracy. Due to the image acquisition process, optical satellite images commonly have an absolute geo-localization accuracy in the order of meters or tens of meters only. On the other hand, images captured by the high-resolution synthetic aperture radar satellite TerraSAR-X can achieve an absolute geo-localization accuracy within a few decimeters and therefore represent a reliable source for absolute geo-localization accuracy improvement of optical data. The main objective of this thesis is to address the challenge of image matching between high resolution optical and synthetic aperture radar (SAR) satellite imagery in order to improve the absolute geo-localization accuracy of the optical images. The different imaging properties of optical and SAR data pose a substantial challenge for a precise and accurate image matching, in particular for the handcrafted feature extraction stage common for traditional optical and SAR image matching methods. Therefore, a concept is required which is carefully tailored to the characteristics of optical and SAR imagery and is able to learn the identification and extraction of relevant features. Inspired by recent breakthroughs in the training of neural networks through deep learning techniques and the subsequent developments for automatic feature extraction and matching methods of single sensor images, two novel optical and SAR image matching methods are developed. Both methods pursue the goal of generating accurate and precise tie points by matching optical and SAR image patches. The foundation of these frameworks is a semi-automatic matching area selection method creating an optimal initialization for the matching approaches, by limiting the geometric differences of optical and SAR image pairs. The idea of the first approach is to eliminate the radiometric differences between the images trough an image-to-image translation with the help of generative adversarial networks and to realize the subsequent image matching through traditional algorithms. The second approach is an end-to-end method in which a Siamese neural network learns to automatically create tie points between image pairs through a targeted training. The geo-localization accuracy improvement of optical images is ultimately achieved by adjusting the corresponding optical sensor model parameters through the generated set of tie points. The quality of the proposed methods is verified using an independent set of optical and SAR image pairs spread over Europe. Thereby, the focus is set on a quantitative and qualitative evaluation of the two tie point generation methods and their ability to generate reliable and accurate tie points. The results prove the potential of the developed concepts, but also reveal weaknesses such as the limited number of training and test data acquired by only one combination of optical and SAR sensor systems. Overall, the tie points generated by both deep learning-based concepts enable an absolute geo-localization improvement of optical images, outperforming state-of-the-art methods.

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