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Please use this identifier to cite or link to this item: http://hdl.handle.net/1974/5982

Title: Localization of Human Pelvis Anatomical Coordinate System Using Ultrasound Registration to Statistical Shape Model

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Keywords: Pelvic Anatomical Coordinate System
Statistical Shape Model
Issue Date: 2010
Series/Report no.: Canadian theses
Abstract: Total Hip Replacement (THR) has become a common surgical procedure in recent years, due to the increase in the aging population with hip osteoarthritis. Identifying the proper orientation of the pelvis is a critical step in accurate placement of the femur prosthesis in the acetabulum in THR. The general approach to localize the orientation of the pelvic anatomical coordinate system (PaCS) is to use intra-operative X-ray fluoroscopy in a specialized interventional radiology facility to guide the procedure. Employing intra-operative ultrasound (US) imaging fused with pre-operative CT scan or fluoroscopy imaging was proposed to eliminate the ionizing radiation of intra-operative X-ray to the patient and the need for radiology facilities in the OR. However, the use of pre-operative imaging exposes patients to accumulative ionizing radiation which is desirable to be eliminated. In this thesis, I propose to replace pre-operative imaging with a statistical shape model (SSM) of the pelvis which is constructed from CT images of patients. An automatic deformable registration of a pelvis anatomical shape model to a sparse set of 2D ultrasound images of the pelvis is presented in order to localize the PaCS. In this registration technique, a set of 2D slices are extracted from the pelvic shape model, based on the approximate location and orientation of a corresponding 2D ultrasound image. The comparison of the shape model slices and ultrasound images is made possible by using an ultrasound simulation technique and a correlation-based similarity metric. During the registration, an instance of the shape model is generated that best matches the ultrasound data. I demonstrate the feasibility of our proposed approach in localizing the PaCS on four patient phantoms and on data from two male human cadavers. None of the test data sets were included in the SSM generation.
Description: Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2010-08-23 17:57:24.708
URI: http://hdl.handle.net/1974/5982
Appears in Collections:Queen's Graduate Theses and Dissertations
Department of Electrical and Computer Engineering Graduate Theses

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