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Image processing of MRI
Němcová, Simona ; Jiřík, Radovan (referee) ; Krátká, Lucie (advisor)
This thesis deals with the cartilage imaging using magnetic resonance. At first, there is mentioned physical principle of the magnetic resonance phenomenon and the most commonly used excitation sequences, followed by the description of the 9.4 T MR imaging system Bruker BioSpec 94/30 USR, which was used for measurement in the practical part. The next part is dedicated to the composition of cartilages and describes the temporomandibular joint, due to its suitability as an object for cartilage imaging. The series of MR scans of temporomandibular joint were taken with different acquisition parameters and evaluated by program designed through the MATLAB software. The program can be used for viewing scanned images, evaluating their contrast and determining the T1 relaxation time of the tissues by creating T1 maps.
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Influence of parcellation atlas on quality of classification in patients with neurodegenerative dissease
Montilla, Michaela ; Lamoš, Martin (referee) ; Gajdoš, Martin (advisor)
The aim of the thesis is to define the dependency of the classification of patients affected by neurodegenerative diseases on the choice of the parcellation atlas. Part of this thesis is the application of the functional connectivity analysis and the calculation of graph metrics according to the method published by Olaf Sporns and Mikail Rubinov [1] on fMRI data measured at CEITEC MU. The application is preceded by the theoretical research of parcellation atlases for brain segmentation from fMRI frames and the research of mathematical methods for classification as well as classifiers of neurodegenerative diseases. The first chapters of the thesis brings a theoretical basis of knowledge from the field of magnetic and functional magnetic resonance imaging. The physical principles of the method, the conditions and the course of acquisition of image data are defined. The third chapter summarizes the graph metrics used in the diploma thesis for analyzing and classifying graphs. The paper presents a brief overview of the brain segmentation methods, with the focuse on the atlas-based segmentation. After a theoretical research of functional connectivity methods and mathematical classification methods, the findings were used for segmentation, calculation of graph metrics and for classification of fMRI images obtained from 96 subjects into the one of two classes using Binary classifications by support vector machines and linear discriminatory analysis. The data classified in this study was measured on patiens with Parkinson’s disease (PD), Alzheimer’s disease (AD), Mild cognitive impairment (MCI), a combination of PD and MCI and subjects belonging to the control group of healthy individuals. For pre-processing and analysis, the MATLAB environment, the SPM12 toolbox and The Brain Connectivity Toolbox were used.
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Influence of parcellation atlas on quality of classification in patients with neurodegenerative dissease
Montilla, Michaela ; Lamoš, Martin (referee) ; Gajdoš, Martin (advisor)
The aim of the thesis is to define the dependency of the classification of patients affected by neurodegenerative diseases on the choice of the parcellation atlas. Part of this thesis is the application of the functional connectivity analysis and the calculation of graph metrics according to the method published by Olaf Sporns and Mikail Rubinov [1] on fMRI data measured at CEITEC MU. The application is preceded by the theoretical research of parcellation atlases for brain segmentation from fMRI frames and the research of mathematical methods for classification as well as classifiers of neurodegenerative diseases. The first chapters of the thesis brings a theoretical basis of knowledge from the field of magnetic and functional magnetic resonance imaging. The physical principles of the method, the conditions and the course of acquisition of image data are defined. The third chapter summarizes the graph metrics used in the diploma thesis for analyzing and classifying graphs. The paper presents a brief overview of the brain segmentation methods, with the focuse on the atlas-based segmentation. After a theoretical research of functional connectivity methods and mathematical classification methods, the findings were used for segmentation, calculation of graph metrics and for classification of fMRI images obtained from 96 subjects into the one of two classes using Binary classifications by support vector machines and linear discriminatory analysis. The data classified in this study was measured on patiens with Parkinson’s disease (PD), Alzheimer’s disease (AD), Mild cognitive impairment (MCI), a combination of PD and MCI and subjects belonging to the control group of healthy individuals. For pre-processing and analysis, the MATLAB environment, the SPM12 toolbox and The Brain Connectivity Toolbox were used.
|
|
|
Image processing of MRI
Němcová, Simona ; Jiřík, Radovan (referee) ; Krátká, Lucie (advisor)
This thesis deals with the cartilage imaging using magnetic resonance. At first, there is mentioned physical principle of the magnetic resonance phenomenon and the most commonly used excitation sequences, followed by the description of the 9.4 T MR imaging system Bruker BioSpec 94/30 USR, which was used for measurement in the practical part. The next part is dedicated to the composition of cartilages and describes the temporomandibular joint, due to its suitability as an object for cartilage imaging. The series of MR scans of temporomandibular joint were taken with different acquisition parameters and evaluated by program designed through the MATLAB software. The program can be used for viewing scanned images, evaluating their contrast and determining the T1 relaxation time of the tissues by creating T1 maps.
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