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Segmentation of hippocampus in MRI data
Kodym, Oldřich ; Chmelík, Jiří (referee) ; Walek, Petr (advisor)
The thesis deals with application of graph-based methods in segmentation of low contrast image data, specifically hippocampus segmentation from magnetic resonance data. Firstly, basics and terminology of graph theory is introduced. Next, minimum graph cut method is explained along with algorithms capable of finding this cut. After that comes the description of its implementation for 2D and 3D image data segmentation. Method was tested on sample data and then implemented as a 3D Slicer software module. Here the method was tested on the hipocampus data of healthy patients as well as patients suffering from Alzheimer’s disease. Most common problems occuring during the segmentation were forshadowed as well as possible ways to solve them.
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Medical image segmentation based on graph cut with shape prior
Kozlová, Dominika ; Jan, Jiří (referee) ; Jakubíček, Roman (advisor)
This thesis deals with a graph-based image segmentation and its improvement by using the information about the shape of the object for creating specific graph architecture (template). There are described basics of the graph theory, which is the basis of the graph segmentation methods. Designed segmentation algorithm was realized in 2D with graphical user interface in MATLAB. For segmentation of volume data, the method was extended into 3D. Implemented method was tested on simulated data and on real CT and MRI images of vertebra and brain. Obtained results were evaluated and compared with the original method without using the template.
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Segmentation of hippocampus in MRI data
Kodym, Oldřich ; Chmelík, Jiří (referee) ; Walek, Petr (advisor)
The thesis deals with application of graph-based methods in segmentation of low contrast image data, specifically hippocampus segmentation from magnetic resonance data. Firstly, basics and terminology of graph theory is introduced. Next, minimum graph cut method is explained along with algorithms capable of finding this cut. After that comes the description of its implementation for 2D and 3D image data segmentation. Method was tested on sample data and then implemented as a 3D Slicer software module. Here the method was tested on the hipocampus data of healthy patients as well as patients suffering from Alzheimer’s disease. Most common problems occuring during the segmentation were forshadowed as well as possible ways to solve them.
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Medical image segmentation based on graph cut with shape prior
Kozlová, Dominika ; Jan, Jiří (referee) ; Jakubíček, Roman (advisor)
This thesis deals with a graph-based image segmentation and its improvement by using the information about the shape of the object for creating specific graph architecture (template). There are described basics of the graph theory, which is the basis of the graph segmentation methods. Designed segmentation algorithm was realized in 2D with graphical user interface in MATLAB. For segmentation of volume data, the method was extended into 3D. Implemented method was tested on simulated data and on real CT and MRI images of vertebra and brain. Obtained results were evaluated and compared with the original method without using the template.
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Segmentation of 3D image data utilising graph representation
Demel, Jan ; Walek, Petr (referee) ; Jan, Jiří (advisor)
This thesis deals with the application of graph theory in image segmentation. There are specifically presented method utilizing graph cuts and extensions of this method. In the first chapter thera are initially explained basics of graph theory that are essential for understanding of the presented method. It is described in the second chapter, including its extensions that use shape priors. In the third chapter there is presented solution which is used for vertebrae lesion segmentation in the CT data sets. Final function is implemented into the program but it can be used also separately. Success rate is described using sensitivity and specificity in the last chapter, there are also examples of results.
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