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Multiple sclerosis detection
Kopuletý, Michal ; Mangová, Marie (referee) ; Uher, Václav (advisor)
This thesis is focused on detecting multiple sclerosis lesions from magnetic resonance images. Correctly retrieved lesions are very important for medical diagnosis. Detection of lesions using machine learning techniques is quite challenging because of large variability in size, shape and position of lesions in the brain. In the practical part is designed base software, which after completion will classify pixels, so that is possible to find lesions of multiple sclerosis. For classification will be used Support vector machine. Theoretical part describes multiple sclerosis, basic operations performed with biomedical images and data classification.
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Two video-processing problems by means of nontraditional methods
Kánský, Antonín ; Mangová, Marie (referee) ; Rajmic, Pavel (advisor)
The aim of this work is to solve two problems from the field of video editing by means of sparse representation of signals. The problematics of the traditional realisation of two effects, which are background separation of and separation background from moving foreground, is clarified here, as well as the problematics of sparse signals. The solutions was achieved through the method of Principal component analysis (PCP). The resulting algorithm is implemented and tested by simulated and real data.
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Interactive software tools for teaching signal processing
Pacas, Ondrej ; Rajmic, Pavel (referee) ; Mangová, Marie (advisor)
This thesis deals with creation of four interactive applications for educational purposes in the field of digital signal processing. The goal of this work is to create four applications which will visually interpretate each of the methods of signal processing. This involves applications for linear regression and least squares method, interpolation and signal reconstruction from its samples, discrete linear convolution and discrete cross-correlation. Applications are created using JavaScript programming language.
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Modelling of perfusion curves in dynamic magnetic resonance
Ochodnický, Erik ; Mangová, Marie (referee) ; Rajmic, Pavel (advisor)
Perfusion MRI can provide information about perfusion characteristics of the observed tissue, which makes it a widely applicable medical procedure. Measuring process of MRI is very time-consuming, and therefore, using classical reconstruction methods, we are often not able to obtain enough samples to accomplish the needed time and space resolution for perfusion analysis. That is why it is necessary to use compressed sensing, which allows reconstruction from under-sampled data by solving an optimization model. In this work, several models for reconstruction of an image sequence are verified on real and artificial data, along with multiple algorithms capable of solving these models. Among the optimization models used in this work are two L+S models with different regularization of the S component that are solved by Forward-Backward and Chambolle-Pock algorithm. The quality of reconstruction for various models was compared especially by their perfusion curves. In the last section, we explore possible modifications of the SASS model in order to increase quality of reconstruction and resistance to under sampling for the purpose of better adaptation for dynamic data.
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Interactive software tools for teaching signal and image processing
Olbert, Jaroslav ; Rajmic, Pavel (referee) ; Mangová, Marie (advisor)
The bachelor’s thesis deals with creating Java applets to support teaching signal and image processing. The theoretical part of the thesis briefly describes the history and advantages of the Java language; subsequently the theories for individual applets and descriptions of the source codes are presented. The practical part consists of the design and creation of applets for linear combinations of images, the least squares method and recommendation of an aiming point on the darts target.
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Graphical user interface for magnetic resonance data reconstruction
Marcin, Michal ; Rajmic, Pavel (referee) ; Mangová, Marie (advisor)
The aim of the bachelor thesis was to create graphical user interface for processing magnetic resonance data. For this purpose, the Matlab development environment was used. The basic principles of magnetic resonance, the ways of scanning and the graphical imaging as well as mathematical methods are described in the first part. The method of compression scanning and imaging of real images using laboratory mice is described in the text. In the second part of the thesis, the graphical user environment and its functions are described. The program is able to simulate and display the Shepp-Logan phantom model according to specified parameters. The created application allows the reconstruction and processing of simulated as well as real data.
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Statistical anomaly detection methods of data communication
Woidig, Eduard ; Mangová, Marie (referee) ; Slavíček, Karel (advisor)
This thesis serves as a theoretical basis for a practical solution to the issue of the use of statistical methods for detecting anomalies in data traffic. The basic focus of anomaly detection data traffic is on the data attacks. Therefore, the main focus is the analysis of data attacks. Within the solving are data attacks sorted by protocols that attackers exploit for their own activities. Each section describes the protocol itself, its usage and behavior. For each protocol is gradually solved description of the attacks, including the methodology leading to the attack and penalties on an already compromised system or station. For the most serious attacks are outlined procedures for the detection and the potential defenses against them. These findings are summarized in the theoretical analysis, which should serve as a starting point for the practical part, which will be the analysis of real data traffic. The practical part is divided into several sections. The first of these describes the procedures for obtaining and preparing the samples to allow them to carry out further analysis. Further described herein are created scripts that are used for obtaining needed data from the recorded samples. These data are were analyzed in detail, using statistical methods such as time series and descriptive statistics. Subsequently acquired properties and monitored behavior is verified using artificial and real attacks, which is the original clean operation modified. Using a new analysis of the modified traffics compared with the original samples and an evaluation of whether it has been some kind of anomaly detected. The results and tracking are collectively summarized and evaluated in a separate chapter with a description of possible further attacks, which were not directly part of the test analysis.
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Modelling of perfusion curves in dynamic magnetic resonance
Ochodnický, Erik ; Mangová, Marie (referee) ; Rajmic, Pavel (advisor)
Perfusion MRI can provide information about perfusion characteristics of the observed tissue, which makes it a widely applicable medical procedure. Measuring process of MRI is very time-consuming, and therefore, using classical reconstruction methods, we are often not able to obtain enough samples to accomplish the needed time and space resolution for perfusion analysis. That is why it is necessary to use compressed sensing, which allows reconstruction from under-sampled data by solving an optimization model. In this work, several models for reconstruction of an image sequence are verified on real and artificial data, along with multiple algorithms capable of solving these models. Among the optimization models used in this work are two L+S models with different regularization of the S component that are solved by Forward-Backward and Chambolle-Pock algorithm. The quality of reconstruction for various models was compared especially by their perfusion curves. In the last section, we explore possible modifications of the SASS model in order to increase quality of reconstruction and resistance to under sampling for the purpose of better adaptation for dynamic data.
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