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Precise segmentation of image data
Svoboda, Jan ; Marcoň, Petr (referee) ; Mikulka, Jan (advisor)
The concern of this thesis is a development of an extension module for 3D Slicer platform. The core of the module is an implementation of a Support Vector Machines classifier, which is used for segmentation of the vertebral column image data provided by the University Hospital Brno. One of the goals of the thesis was resampling and registration of these image sequences. CT volumes provided solid contrast and were used as a reference for gaining properly segmented groups of vertebrae. Due to the low quality of the MRI volumes image data, segmentation of MRI images was not completely succesful. The extension module scripted in Python language can be seen as a tool and can be used in the future for different datasets.
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3D Slicer Extension for Tomographic Images Segmentation
Chalupa, Daniel ; Jakubíček, Roman (referee) ; Mikulka, Jan (advisor)
This work explores machine learning as a tool for medical images' classification. A literary research is contained concerning both classical and modern approaches to image segmentation. The main purpose of this work is to design and implement an extension for the 3D Slicer platform. The extension uses machine learning to classify images using set parameters. The extension is tested on tomographic images obtained by nuclear magnetic resonance and observes the accuracy of the classification and usability in practice.
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Segmetation of tomographic data in 3D Slicer
Korčuška, Robert ; Dvořák, Pavel (referee) ; Mikulka, Jan (advisor)
This thesis contains basic theoretical information about SVM-based image segmentation and data classification. Basic information about 3D Slicer software are presented. Aspects of medical images segmentation are described. Workplan and implemetation of SVM method for MRI segmentation in 3D Slicer sofware as extension module is created. SVM method is compared with simple segmentation algorithms included in 3D Slicer. Quality of segmentation, based on SVM, tested on real subjects is experimentaly demonstrated.
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Analysis of the use of 3D slicer SW for computational modeling in biomechanics
Kratochvílová, Hana ; Marcián, Petr (referee) ; Vosynek, Petr (advisor)
This thesis deals with a freeware program called 3D Slicer and its usefulness in the area of biomechanics. The introduction part describes the scope of the program and its functions. The next chapter shows a step-by-step modelling of a bone geometry using CT images and a comparison of geometries of bones created with different grayscale level using a program Gom Inspect. The last part focuses on importing geometries created in 3D Slicer, specifically femur and pelvis, into FEM platform ANSYS Workbench for preprocessing and defining femur’s stress and deformation characteristics.
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Supporting processing of tomographic data for 3D Slicer
Dašek, Filip ; Harabiš, Vratislav (referee) ; Čmiel, Vratislav (advisor)
Thesis summarize available softwares for visualization and segmentation of medical data. Main focus is on open source software 3D Slicer, where all main modules and functions for processing and segmentation of 3D images are described. The reader is also acquaint with posibility to create new modules and function in Python. In practical part of thesis new modul is created. Modul is focused on fast and effective segmentation of heart and their parts by using watershed method. Part of the module are tools for preprocessing of CT images and automatic post-processing. GUI was implemented and created with QT designer. Thesis containts testing and presenting segmented data. Segmentation by presented modul generaly achieves better results then options offered by 3D Slicer. In the last part of thesis is presented proposal for usage of supporting funtions for fast and efective segmentation of heart and his parts.
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Supervised Segmentation For 3D Slicer
Chalupa, Daniel
The purpose of this work is to introduce an extendable framework for training and usage of machine learning algorithms. This framework is bundled in an extension for 3D Slicer that is to be used for medical images segmentation. An example usage of the extension is also provided.
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Precise segmentation of image data
Svoboda, Jan ; Marcoň, Petr (referee) ; Mikulka, Jan (advisor)
The concern of this thesis is a development of an extension module for 3D Slicer platform. The core of the module is an implementation of a Support Vector Machines classifier, which is used for segmentation of the vertebral column image data provided by the University Hospital Brno. One of the goals of the thesis was resampling and registration of these image sequences. CT volumes provided solid contrast and were used as a reference for gaining properly segmented groups of vertebrae. Due to the low quality of the MRI volumes image data, segmentation of MRI images was not completely succesful. The extension module scripted in Python language can be seen as a tool and can be used in the future for different datasets.
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|
|
Analysis of the use of 3D slicer SW for computational modeling in biomechanics
Kratochvílová, Hana ; Marcián, Petr (referee) ; Vosynek, Petr (advisor)
This thesis deals with a freeware program called 3D Slicer and its usefulness in the area of biomechanics. The introduction part describes the scope of the program and its functions. The next chapter shows a step-by-step modelling of a bone geometry using CT images and a comparison of geometries of bones created with different grayscale level using a program Gom Inspect. The last part focuses on importing geometries created in 3D Slicer, specifically femur and pelvis, into FEM platform ANSYS Workbench for preprocessing and defining femur’s stress and deformation characteristics.
|
|
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3D Slicer Extension for Tomographic Images Segmentation
Chalupa, Daniel ; Jakubíček, Roman (referee) ; Mikulka, Jan (advisor)
This work explores machine learning as a tool for medical images' classification. A literary research is contained concerning both classical and modern approaches to image segmentation. The main purpose of this work is to design and implement an extension for the 3D Slicer platform. The extension uses machine learning to classify images using set parameters. The extension is tested on tomographic images obtained by nuclear magnetic resonance and observes the accuracy of the classification and usability in practice.
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