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Application of optimisation methods for MRI data segmentation
Olešová, Kristína ; Mézl, Martin (referee) ; Chmelík, Jiří (advisor)
This thesis deals with a segmentation of brain tissues from MRI image data and its implementation in MATLAB. Segmentation problematic is described with attention to formulating segmentation as optimization problem and segmentation of given images with different metaheuristic algorithm consequently. This approach was chosen due to information from last specialized publications, where it was accentuated for its fast computational speed and universality. This thesis tries to prove this statement with segmentation of brain images with brain tumours that have different types, number, stage of illness and phase of therapy.
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Classification of glioma grading in brain MRI
Olešová, Kristína ; Mézl, Martin (referee) ; Chmelík, Jiří (advisor)
This thesis deals with a classification of glioma grade in high and low aggressive tumours and overall survival prediction based on magnetic resonance imaging. Data used in this work is from BRATS challenge 2019 and each set contains information from 4 weighting sequences of MRI. Thesis is implemented in PYTHON programming language and Jupyter Notebooks environment. Software PyRadiomics is used for calculation of image features. Goal of this work is to determine best tumour region and weighting sequence for calculation of image features and consequently select set of features that are the best ones for classification of tumour grade and survival prediction. Part of thesis is dedicated to survival prediction using set of statistical tests, specifically Cox regression
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Classification of glioma grading in brain MRI
Olešová, Kristína ; Mézl, Martin (referee) ; Chmelík, Jiří (advisor)
This thesis deals with a classification of glioma grade in high and low aggressive tumours and overall survival prediction based on magnetic resonance imaging. Data used in this work is from BRATS challenge 2019 and each set contains information from 4 weighting sequences of MRI. Thesis is implemented in PYTHON programming language and Jupyter Notebooks environment. Software PyRadiomics is used for calculation of image features. Goal of this work is to determine best tumour region and weighting sequence for calculation of image features and consequently select set of features that are the best ones for classification of tumour grade and survival prediction. Part of thesis is dedicated to survival prediction using set of statistical tests, specifically Cox regression
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Application Of Optimisation Methods For Mri Data Segmentation
Olešová, Kristína
The presented paper describes a use of metaheuristic algorithm for medical image segmentation. First section is dedicated to a brief introduction to the principles of this kind of segmentation and second section is used for description of used algorithms and used approaches to segmentation. Next sections are used for presentations of achieved results.
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Application of optimisation methods for MRI data segmentation
Olešová, Kristína ; Mézl, Martin (referee) ; Chmelík, Jiří (advisor)
This thesis deals with a segmentation of brain tissues from MRI image data and its implementation in MATLAB. Segmentation problematic is described with attention to formulating segmentation as optimization problem and segmentation of given images with different metaheuristic algorithm consequently. This approach was chosen due to information from last specialized publications, where it was accentuated for its fast computational speed and universality. This thesis tries to prove this statement with segmentation of brain images with brain tumours that have different types, number, stage of illness and phase of therapy.
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