University of Illinois Urbana-Champaign

Information extraction algorithms based on optical coherence tomography data

Xu, Yang

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XU-DISSERTATION-2017.pdf

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https://hdl.handle.net/2142/99193

Description

Title
Information extraction algorithms based on optical coherence tomography data
Author(s)
Xu, Yang
Issue Date
2017-10-23
Director of Research (if dissertation) or Advisor (if thesis)
Carney, P. Scott
Doctoral Committee Chair(s)
Carney, P. Scott
Committee Member(s)
  • Eden, J. Gary
  • Singer, Andrew C.
  • Oelze, Michael L.
  • Gruev, Viktor
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2018-03-13T15:21:05Z
Keyword(s)
  • Optical coherence tomography
  • Interferometric synthetic aperture microscopy
Abstract
In this dissertation, we present several algorithms developed for optical coherence tomography (OCT) datasets. We show that by incorporating physical modeling, mathematical analysis and signal processing, it is possible to uncover the huge potential hidden in the raw OCT data. The automated Interferometric Synthetic Aperture Microscopy algorithm uses image sharpness metrics to automatically search for the optimal parameters for each step of the processing, resulting in near-optimal reconstruction comparable to images manually tuned by experts. The filtering algorithm for Doppler OCT images combines mathematical optimization with GPU parallel programming, and drastically increases the dynamic range of Doppler OCT velocity maps, making Doppler OCT potentially applicable to faster blood vessels such as the carotids. The susceptibility tensor ISAM algorithm converts the complicated multipolarization scattering and detection problem into a linear algebra problem and solves it through regularized least square method, which enables us to indirectly measure all nine components of the susceptibility tensor for clusters of discrete particles. We also discuss the potential method to extend susceptibility tensor ISAM to continuous samples, which promises better contrast between tissue types and insights into the anisotropic structure inside biological tissue.
Graduation Semester
2017-12
Type of Resource
text
Permalink
http://hdl.handle.net/2142/99193
Copyright and License Information
Copyright 2017 Yang Xu

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