Publication Date

12-2009

Date of Final Oral Examination (Defense)

10-23-2009

Type of Culminating Activity

Thesis

Degree Title

Master of Science in Computer Science

Department

Computer Science

Supervisory Committee Chair

Amit Jain, Ph.D.

Supervisory Committee Co-Chair

Elisa H. Barney Smith, Ph.D.

Supervisory Committee Member

Timothy Andersen, Ph.D.

Abstract

Fluoroscopic analysis of knee joint kinematics involves accurately determining the position and orientation of bones in the knee joint. This data can be derived using the static 3-D CT scan images and 2-D video fluoroscopy images together. This involves generating hypothetical digitally reconstructed radiographs (DRR) from the CT scan image with known position and orientation and comparing them to the original fluoroscopic frame. This represents a search problem in which, among all the DRRs possible from a CT image, the image that most closely matches the target fluoroscopy frame of the knee joint has to be found.

Each image in the search space differs from another by the set of position and orientation values. Position is defined by the x, y and z co-ordinates in the Cartesian coordinate system. Orientation is defined by the values of azimuth, elevation and rotation. Therefore, this constitutes a six dimensional search problem, and using a brute force method to search for the target image can take a tremendous amount of time. The fact that it is difficult to order or categorize the set of six values adds the complexity of the search. Further, previous research conducted by Scott et al. [1] suggests that even good sequential search algorithms such as the sequential Monte Carlo method can be very time-consuming. Therefore, a better search solution suitable for this kind of 6-D search that provides a significant speedup has to be found. This thesis explores using Swarm Intelligence (SI) techniques in a parallel computing environment to create a test bed for fluoroscopic analysis and increase the speed of the process. Parallel programs are developed using two SI techniques: Bees Algorithm and Particle Swarm Optimization technique.

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