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Please use this identifier to cite or link to this item:
http://hdl.handle.net/1974/5689
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| Title: | Feature Based Registration of Ultrasound and CT Data of a Scaphoid |
| Authors: | Koslowski, Brian |
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| Keywords: | Feature-Based Registration
Ultrasound
CT
Scaphoid |
| Issue Date: | 2010 |
| Series/Report no.: | Canadian theses |
| Abstract: | Computer assisted surgery uses a collection of different techniques including but not limited to: CT-guided, fluoroscopy-guided, and ultrasound-guided imaging which allows medical staff to view bony anatomy of a patient in relation to surgical tools on a computer screen. By providing this visual data to surgeons less invasive surgeries can be performed on a patient's fractured scaphoid. The data required for a surgeon to perform a minimally invasive surgery while looking only at a computer screen, and not directly at a patient's anatomy, will be provided by CT and ultrasound data. We will discuss how ultrasound and CT data can be used together to allow a minimally invasive surgery of the scaphoid to be performed.
In this thesis we will explore two techniques of registering segmented ultrasound images to CT data; an Iterative Closest Point (ICP) approach, and an Unscented Kalman Filter-based Registration (UKF). We use two different ultrasound segmentation methods; a semi-automatic segmentation, and a Bayesian segmentation technique. The segmented ultrasound data is then registered to a CT volume. The success or failure of the
registrations is measured by the error calculated in mapping the corresponding land-
marks to one another and calculating the target registration error. The results show that the Unscented Kalman Filter-based registration using the Bayesian segmentation of ultrasound images has the least registration error, and has the most robustness to error in initial alignment of the two data sets. |
| Description: | Thesis (Master, Computing) -- Queen's University, 2010-05-28 11:17:31.934 |
| URI: | http://hdl.handle.net/1974/5689 |
| Appears in Collections: | Queen's Theses & Dissertations
Computing Graduate Theses
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