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Improved active shape models for segmentation of the prostate on MR imagery
Descriptive
TypeOfResource
Text
TitleInfo
(ID = T-1)
Title
Improved active shape models for segmentation of the prostate on MR imagery
Identifier
ETD_2785
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056806
Language
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(authority = ISO639-2);
(type = code)
eng
Genre
(authority = marcgt)
theses
Subject
(ID = SBJ-1);
(authority = RUETD)
Topic
Biomedical Engineering
Subject
(ID = SBJ-2);
(authority = ETD-LCSH)
Topic
Prostate--Magnetic resonance imaging
Subject
(ID = SBJ-3);
(authority = ETD-LCSH)
Topic
Magnetic resonance microscopy
Abstract
(type = abstract)
Segmentation aims to determine which locations within an image contain the object of interest. Segmentation of the prostate boundary on clinical images is useful in a large number of applications including calculation of prostate volume pre- and post-treatment, detection of extra-capsular spread, and creation of patient-specific anatomical models. Manual segmentation of the prostate boundary is, however, time consuming and subject to inter- and intra-reader variability. T2-weighted (T2-w) Magnetic Resonance (MR) structural imaging (MRI) and MR Spectroscopy (MRS) have recently emerged as promising modalities for detection of prostate cancer in vivo. With the recent advance in prostate imagery, we have generated an accurate prostate segmentation system for MR imagery. Our system builds upon the popular Active Shape Model (ASM) framework, in which a statistical description of the shape is first generated, after which an appearance of the object of interest is modeled. In our system, the shape model can be generated in either 2D or 3D, and is defined by a set of anatomical landmarks. For the appearance model, we offer several improvements. We generated statistical texture features of the prostate images, and use those features to overcome limitations of solely using intensities. In addition, we use intelligent feature selection algorithms including forward feature selection and adaboost to determine which features to include in our segmentation system. The statistical appearance models are not modeled as a simple Gaussian distribution, but rather as a sum of Gaussians, resulting in more accurate models. In 2D, a local appearance model is generated for each landmark location on the prostate border. However, in 3D this is infeasible, so we generate a global appearance model describing the voxels within the prostate. The 2D ASM resulted in a Dice similarity coefficient (DSC) of 0.85, while our 3D system resulted in a DSC of 0.89 (over 56 and 37 studies respectively). This is comparable to other state of the art prostate MR segmentation schemes. Finally, we have shown that in the specific application of prostate volume estimation, our system performs more accurate volume estimations than currently employed clinical models. Our system achieved a correlation (R^2 value) with the ground truth volume of 0.82 while the clinical model achieved an R^2 value of 0.70. Our system had a volume fraction of 1.05 in comparison to the ground truth volume, while the clinical model achieved a volume fraction of 1.14. Overall, we have developed an efficient, accurate, and useful prostate segmentation scheme for MR imagery.
PhysicalDescription
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electronic resource
Extent
ix, 44 p. : ill.
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application/pdf
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text/xml
Note
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M.S.
Note
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Includes bibliographical references
Note
(type = vita)
Includes vita
Note
(type = statement of responsibility)
by Robert James Toth
Name
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Toth
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Robert James
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1987-
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author
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Robert Toth
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Madabhushi
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chair
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Advisory Committee
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Anant Madabhushi
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(type = family)
Shinbrot
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(type = given)
Troy
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internal member
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Advisory Committee
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Troy Shinbrot
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(type = personal)
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(type = family)
Boustany
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(type = given)
Nada
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internal member
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Advisory Committee
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Nada Boustany
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(type = family)
Ganesan
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(type = given)
Shridar
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outside member
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Advisory Committee
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Shridar Ganesan
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NamePart
Rutgers University
Role
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degree grantor
Name
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NamePart
Graduate School - New Brunswick
Role
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school
OriginInfo
DateCreated
(qualifier = exact)
2010
DateOther
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2010-10
Place
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(type = code)
xx
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
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ETD
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(type = host)
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Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/T3DJ5FB3
Genre
(authority = ExL-Esploro)
ETD graduate
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Rights
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(AUTHORITY = GS);
(ID = rulibRdec0006)
The author owns the copyright to this work.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
RightsHolder
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(type = personal)
Name
FamilyName
Toth
GivenName
Robert
Role
Copyright Holder
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Type
Permission or license
DateTime
2010-07-21 18:25:51
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Copyright holder
Name
Robert Toth
Affiliation
Rutgers University. Graduate School - New Brunswick
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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.
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Technical
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ETD
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application/pdf
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application/x-tar
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1208320
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b1f1bd6c4c9fa910c7f2a6e7b3322bf319f9b36c
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Statistical Profile