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Automatic and interactive segmentations using deformable and graphical models

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TitleInfo
Title
Automatic and interactive segmentations using deformable and graphical models
Name (type = personal)
NamePart (type = family)
Uzunbas
NamePart (type = given)
Mustafa Gokhan
NamePart (type = date)
1983-
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Mustafa Gokhan Uzunbas
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RoleTerm (authority = RULIB)
author
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Metaxas
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Dimitris
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Dimitris Metaxas
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Advisory Committee
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chair
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Ahmed
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Ahmed Elgammal
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Advisory Committee
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internal member
Name (type = personal)
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Bekris
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Kostas
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Kostas Bekris
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Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Shen
NamePart (type = given)
Dinggang
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Dinggang Shen
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (encoding = w3cdtf); (qualifier = exact)
2015
DateOther (qualifier = exact); (type = degree)
2015-01
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2015
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Image segmentation i.e. dividing an image into regions and categories is a classic yet still challenging problem. The key to success is to use/develop the right method for the right appli- cation. In this dissertation, we aim to develop automatic and interactive segmentation methods for different types of tissues that are acquired at different scales and resolutions from different medical imaging modalities such as Magnetic Resonance (MR), Computed Tomography (CT) and Electron Microscopy (EM) imaging. First, we developed an automated segmentation method for segmenting multiple organs simultaneously from MR and CT images. We propose a hybrid method that takes advantage of two well known energy-minimization-based approaches combined in a unified framework. We validate this proposed method on cardiac four-chamber segmentation from CT and knee joint bones segmentation from MR images. We compare our method with other existing techniques and show certain improvements and advantages. Second, we developed a graph partitioning algorithm for characterizing neuronal tissue structurally and contextually from EM images. We propose a multistage decision mechanism that utilizes differential geometric properties of objects in a cellular processing context. Our results indicate that this proposed approach can successfully partition images into structured segments with minimal expert supervision and can potentially form a basis for a larger scale volumetric data interpretation. We compare our method with other proposed methods in a workshop challenge and show promising results. Third, we developed an efficient learning-based method for segmentation of neuron struc- tures from 2D and 3D EM images. We propose a graphical-model-based framework to do inference on hierarchical merge-tree of image regions. In particular, we extract the hierarchy of regions in the low level, design 2D and 3D discriminative features to extract higher level information and utilize a Conditional Random Field based parameter learning on top of it. The effectiveness of the proposed method in 2D is demonstrated by comparing our method with other methods in a workshop challenge. Our method outperforms all participant methods ex- cept one. In 3D, we compare our method to existing methods and show that the accuracy of our results are comparable to state-of-the-art while being much more efficient. Finally, we extended our inference algorithm to a proofreading framework for manual cor- rections of automatic segmentation results. We propose a very efficient and easy-to-use inter- face for high resolution 3D EM images. In particular, we utilize the probabilistic confidence level of the graphical model to guide the user during interaction. We validate the effective- ness of this framework by robot simulations and demonstrate certain advantages compared to baseline methods.
Subject (authority = RUETD)
Topic
Computer Science
Subject (authority = ETD-LCSH)
Topic
Image segmentation
Subject (authority = ETD-LCSH)
Topic
Image analysis
Subject (authority = ETD-LCSH)
Topic
Electron microscopy
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_6091
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xvii, 94 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Mustafa Gokhan Uzunbas
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3W37Z2B
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Uzunbas
GivenName
Mustafa
MiddleName
Gokhan
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2014-12-19 22:16:02
AssociatedEntity
Name
Mustafa Uzunbas
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
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.
RightsEvent
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2015-01-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2017-01-30
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after January 30th, 2017.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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ETD
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windows xp
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