Advisor: Nassir Navab
Supervision by: Stefanie Demirci, Max Baust
Automatic detection of the stent graft in interventional 2D and 3D image data is the essential basis to achieve these goals and, thus, constitutes the main concern of this diploma thesis. The use of model-based rigid and deformable registration techniques for stent graft extraction from medical images is evaluated along with the necessary preprocessing steps to find a proper initialization for the registration algorithm. The latter includes dominant local direction analysis using steerable filters as well as the use of distance maps to improve the registration results. The results are evaluated using synthetic test data and real patient data.
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Title: | Model-based Segmentation of Abdominal Aortic Stent Grafts in 2D and 3D Interventional Images |
Abstract: | Automatic detection of the stent graft in interventional 2D and 3D image data is the essential basis to achieve these goals and, thus, constitutes the main concern of this diploma thesis. The use of model-based rigid and deformable registration techniques for stent graft extraction from medical images is evaluated along with the necessary preprocessing steps to find a proper initialization for the registration algorithm. The latter includes dominant local direction analysis using steerable filters as well as the use of distance maps to improve the registration results. The results are evaluated using synthetic test data and real patient data. |
Student: | Markus Urban |
Director: | Nassir Navab |
Supervisor: | Stefanie Demirci, Max Baust |
Type: | DA/MA/BA |
Area: | Registration / Visualization, Segmentation, Medical Imaging |
Status: | finished |
Start: | 2009/02/15 |
Finish: | 2009/08/15 |
Thesis (optional): | |
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