Title page for ETD etd-05062010-114013

Type of Document Master's Thesis
Author Shen, Yuan
URN etd-05062010-114013
Title Feature Extraction and Feasibility Study on CT Image Guided Colonoscopy
Degree Master of Science
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Wyatt, Christopher L. Committee Chair
Baumann, William T. Committee Member
Stilwell, Daniel J. Committee Member
  • Feature Extraction
  • Image Guided Colonoscopy
  • SLAM
  • Video Feature Tracking
Date of Defense 2010-04-22
Availability unrestricted
Computed tomographic colonography(CTC), also called virtual colonoscopy, uses CT scanning and computer post-processing to create two dimensional images and three dimensional virtual views inside of the colon. Computer-aided polyp detection(CAPD) automatically detects colonic polyps and presents them to the user in either a first or second reader paradigm, with a goal reducing examination time while increasing the detection sensitivity. During colonoscopy, the endoscopists use the colonoscope inside of a patient's colon to target potential polyps and validate CAPD found ones. However, there is no direct information linking between CT images and the real-time optical colonoscopy(OC) video provided during the operation, thus endoscopists need to rely largely on their past experience to locate and remove polyps. The goal of this research project is to study the feasibility of developing an image guided colonoscopy(IGC) system that combines CTC images, real-time colonoscope position measurements, and video stream to validate and guide the removal of polyps found in CAPD. System would ease polyp level validation of CTC and improve the accuracy and efficiency of guiding the endoscopist to the target polyps. In this research project, a centerline based matching algorithm has been designed to estimate, in real time, the relative location of the colonoscope in the virtual colonoscopy environment. Furthermore, the feasibility of applying online simultaneous localization and mapping(SLAM) into CT image guided colonoscopy has been evaluated to further improve the performance of localizing and removing the pre-defined target polyps. A colon phantom is used to provide a testing setup to assess the performance of the proposed algorithms.
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