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Compressive endoscopy - a computational imaging approach for fiber-bundle-based imaging systems

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Title
Compressive endoscopy - a computational imaging approach for fiber-bundle-based imaging systems
Name (type = personal)
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Dumas
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John Paul
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1991-
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John Paul Dumas
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author
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Pierce
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Mark C
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Mark C Pierce
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Advisory Committee
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chair
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Nada N
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Nada N Boustany
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Advisory Committee
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internal member
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Hacihaliloglu
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Ilker
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Ilker Hacihaliloglu
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Advisory Committee
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internal member
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Bajwa
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Waheed U
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Waheed U Bajwa
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Advisory Committee
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outside member
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Rutgers University
Role
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degree grantor
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School of Graduate Studies
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school
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Text
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theses
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2019
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2019-10
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English
Abstract (type = abstract)
Compressed sensing (CS) is a signal processing technique that provides a theoretical framework for accurately reconstructing discrete signals from fewer samples than traditionally dictated by the Shannon-Nyquist theorem. In the context of imaging, CS enables the recovery of images with more resolved points than pixels in the physical sensor. This capability is appealing for minimally invasive biomedical imaging applications that suffer from poor image quality due to inherent constraints on the size and type of hardware that can be used. The goal of this dissertation is to adapt the CS framework for use in endoscopy platforms, providing a path toward higher resolution minimally invasive imaging.
Endoscopes commonly use coherent fiber optic bundles to facilitate in vivo imaging. The image quality with these fiber-bundle-based endoscopes is limited because of manufacturing challenges that restrict achievable fiber density and spacing. Chapter 1 reviews endoscopy technologies and current fiber-bundle- based imaging techniques. The general field of computational imaging is then discussed, including a specific focus on CS-based and spectral imaging approaches that may overcome limitations in fiber bundle imaging.
Chapter 2 identifies and addresses some practical challenges that are not anticipated by CS theory or simulations. Computational imaging based on the CS framework, or compressive imaging (CI), was evaluated with a test platform that introduced intensity modulation at a conjugate image plane. It was demonstrated that a CS model accounting for system-specific practical limitations, like optical aberration, is an efficient way to implement highly parallel CI.
An imaging architecture with intensity modulation at a conjugate image plane is one approach for CI, but the development of different CS mathematical models has given rise to various different CI architectures. Chapter 3 provides a comparison of different architectures with the application of endoscopy in mind. An experimental comparison of two candidate architectures was performed, and it was determined that an architecture with coded masks at a conjugate image plane is a good option for translation to endoscopy.
Chapters 2 and 3 developed imaging methods for CI in test platforms where image quality was limited by the number of pixels in a low-resolution sensor. Chapter 4 translates these methods for fiber-bundle-based imaging where image quality is limited by the number of fibers in an imaging bundle. The fiber bundle was considered as a low-resolution sensor array where the number of resolved points in an image is limited by the number of fibers in the bundle. CI was evaluated in a fiber-bundle-based imaging platform for compressive endoscopy, which was demonstrated for fluorescence imaging and resolved 17 points for each fiber in the bundle.
While CI resolves multiple pixels within the diameter of each fiber in thebundle, inter-fiber cladding that binds fibers together blocks information that is not recovered by traditional CI methods. Chapter 4 presents a solution to this missing information problem using spectral coding with a distal prism to ensure information from all sample positions is transmitted through the fibers and captured by proximal color camera. Integrating spectral coding into the compressive endoscopy platform allowed for image reconstruction that filled in 80% of the missing information. Additionally, spectral coding implemented as a standalone technology without CI generated images with three times more resolved points than images from traditional fiber-bundle-based imaging methods. This snapshot spectral coding approach improves image quality in fiber-bundle-based imaging without requiring distal electromechanical components. The computational imaging methods developed here serve as an initial step toward analyzing tissue morphology using CS principles and spectral coding for in vivo optical biopsy.
Subject (authority = RUETD)
Topic
Biomedical Engineering
Subject (authority = local)
Topic
Computational imaging
Subject (authority = LCSH)
Topic
Endoscopy
Subject (authority = LCSH)
Topic
Diagnostic imaging
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Rutgers University Electronic Theses and Dissertations
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ETD_10067
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application/pdf
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text/xml
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1 online resource (x, 114 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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Identifier (type = doi)
doi:10.7282/t3-ndf9-0215
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Dumas
GivenName
John Paul
Role
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RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-05-31 13:53:09
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Name
John Paul Dumas
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Affiliation
Rutgers University. School of Graduate Studies
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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Type
Embargo
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2019-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2021-10-30
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after October 30th, 2021.
Copyright
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Copyright protected
Availability
Status
Open
Reason
Permission or license
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