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A hierarchical spectral clustering and non-linear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy

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Text
TitleInfo (ID = T-1)
Title
A hierarchical spectral clustering and non-linear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy
SubTitle
PartName
PartNumber
NonSort
Identifier
ETD_1324
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000050467a
Language (objectPart = )
LanguageTerm (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--Cancer
Subject (ID = SBJ-3); (authority = ETD-LCSH)
Topic
Magnetic resonance imaging
Abstract
Magnetic Resonance Spectroscopy (MRS) is a unique non-invasive method which has recently been shown to have great potential in screening of prostate cancer (CaP). MRS provides functional information regarding the concentrations of different biochemicals present in the prostate at single or multiple locations within a rectangular grid of spectra superposed on the structural T2-weighted Magnetic Resonance Imaging (MRI). Changes in relative concentration of specific metabolites including choline, creatine and citrate compared to "normal" levels is highly indicative of the presence of CaP. Most previous attempts at developing computerized schemes for automated prostate cancer detection using MRS have been centered on developing peak area quantification algorithms. These methods seek to obtain area under peaks corresponding to choline, creatine and citrate which is then used to compute relative concentrations of these metabolites. However, manual identification of metabolite peaks on the MR spectra, let alone via automated algorithms, is a challenging problem on account of low SNR, baseline irregularity, peak-overlap, and peak distortion. In this thesis work a novel computer aided detection (CAD) scheme for prostate MRS is presented that integrates non-linear dimensionality reduction (NLDR) with an unsupervised hierarchical clustering algorithm to automatically identify cancerous spectra. The methodology comprises of two specific aims. Aim 1 is to first automatically localize the prostate region followed in Aim 2 by automated cancer detection on the prostate obtained in Aim 1. In Aim 1, a hierarchical spectral clustering algorithm is used to distinguish between informative and non-informative spectra in order to localize the region of interest (ROI) corresponding to the prostate. Once the prostate ROI is localized, in Aim 2, a non-linear dimensionality reduction (NLDR) scheme in conjunction with a replicated k-means clustering algorithm is used to automatically discriminate between 3 classes of spectra (normal, CaP, and intermediate tissue classes). Results of qualitative and quantitative evaluation of the methodology over 18 1.5 Tesla (T) in-vivo prostate T2-w and MRS studies obtained from the multi-site, multi-institutional ACRIN trial, for which corresponding histological ground truth of spatial extent of CaP is available, reveal that the CAD scheme has a high detection sensitivity (89.60) and specificity (78.98). Results further suggest that the CAD scheme has a higher detection accuracy compared to such commonly used MRS analysis schemes as z-score and PCA.
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electronic resource
Extent
xiv, 50 p. : ill.
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application/pdf
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Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references (p. 47-49).
Note (type = statement of responsibility)
by Pallavi Tiwari
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Tiwari
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Pallavi
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author
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Pallavi Tiwari
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Cai
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Li
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chair
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Advisory Committee
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Li Cai
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Madabhushi
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internal member
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Advisory Committee
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Anant Madabhushi
Name (ID = NAME-4); (type = personal)
NamePart (type = family)
Kim
NamePart (type = given)
Sobin
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internal member
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Advisory Committee
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Sobin Kim
Name (ID = NAME-5); (type = personal)
NamePart (type = family)
Rosen
NamePart (type = given)
Mark
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outside member
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Advisory Committee
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Mark Rosen
Name (ID = NAME-1); (type = corporate)
NamePart
Rutgers University
Role
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degree grantor
Name (ID = NAME-2); (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB); (type = )
school
OriginInfo
DateCreated (point = ); (qualifier = exact)
2008
DateOther (qualifier = exact); (type = degree)
2008-10
Place
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xx
Location
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NjNbRU
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Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
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TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Identifier (type = doi)
doi:10.7282/T3N58MNQ
Genre (authority = ExL-Esploro)
ETD graduate
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Copyright
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Availability
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Open
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Non-exclusive ETD license
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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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