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Title page for ETD etd-11072007-144835

Type of Document Master's Thesis
Author Garg, Ishita
Author's Email Address ishita.garg@vanderbilt.edu
URN etd-11072007-144835
Title A computational approach to pre-align point cloud data for surface registration in image guided liver surgery
Degree Master of Science
Department Biomedical Engineering
Advisory Committee
Advisor Name Title
Robert L. Galloway Committee Chair
Michael I. Miga Committee Member
  • iterative closest point
  • anatomical features
  • initial alignment
  • image guided surgery
  • liver
  • abdominal procedures
  • surface-based registration
  • Liver -- Surgery
  • Computer-assisted surgery
  • Liver -- Imaging
  • Stereotaxic techniques
Date of Defense 2007-12-07
Availability unrestricted
Image to physical space registration is a very challenging problem in image guided surgical procedures for the liver due to deformation and paucity of prominent surface anatomical landmarks. Iterative closest point (ICP) algorithm, the surface registration method used for registering the intraoperative laser range scanner (LRS) data with the preoperative CT data in image guided liver surgery, requires a good starting pose to reduce the number of iterations. Currently anatomical landmarks such as vessel bifurcations are used for an initial registration. This paper presents a computational approach to obtain the initial alignment that would reduce contact with probes for registration during surgical procedures. A priori user information about the anatomical orientation of the liver is incorporated and used to orient the point clouds for segmented CT data and LRS liver data. Four points are computationally selected on the anatomical anterior surface of CT point cloud data and corresponding points are localized on the LRS data using the orientation information. These four points are then used to find the rigid transformation using the singular value decomposition method. Nine datasets were tested using the computational approach and the results were compared using the anatomical landmarks method as the "gold standard". Seven of the nine datasets converged to the same solution using both the methods. The computational method, being an approximated approach may increase the number of iterations to converge to the solution. However since the method does not require precise localization of anatomical landmarks, it could potentially reduce OR time.
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