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Feature extraction and matching in content-based retrieval of functional magnetic resonance images
Descriptive
TypeOfResource
Text
TitleInfo
Title
Feature extraction and matching in content-based retrieval of functional magnetic resonance images
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.15781
Identifier
ETD_426
Identifier
(type = doi)
doi:10.7282/T3PG1S6Q
Language
LanguageTerm
(authority = ISO 639-3:2007)
English
Genre
(authority = marcgt)
theses
Subject
(authority = RUETD)
Topic
Computer Science
Subject
(authority = ETD-LCSH)
Topic
Magnetic resonance imaging
Abstract
Functional Magnetic Resonance Imaging (fMRI) has become a widely used technique in neuroscience research. Brain regions corresponding to certain cognitive functionalities can be located by studying the intensity change in a series of 3D brain scans.
Although fMRI has been widely studied, little attention has been paid to content-based (``content'' means the explicit or implicit cognitive process) retrieval of images despite the existence of databases equipped with textual description (fMRIDC). Content-based retrieval is potentially useful in discovering brain activation patterns, and in diagnoses by comparing observed patterns with those of known diseases, leading to clinical applications.
We conducted a comprehensive investigation of feature extraction and similarity measures used in several research communities (including information retrieval (IR), signal processing, and computer vision(CV)), to set up a content-based
retrieval framework for a large, heterogeneous database. We developed methods for both hypothesis-based (stimulus known) and hypothesis-free (stimulus unknown) schemes. For the former, we adapted and extended an adaptive Finite Impulse Response (FIR) Model to get a more robust estimation of the activation level of brain regions. We then relaxed the assumption that the brain responds as a linear time-invariant (LTI) system, by using a 4-parameter ordinary differential equation to model brain responses. We then evaluated a number of similarity measures used in IR and CV, such as Latent Semantic Indexing (LSI), TFIDF, and Mahalanobis distance, etc. For the latter, we used a heuristic to select independent components with low mean temporal frequency, and applied a maximum weight bipartite matching technique to integrate component-level similarity and give a more robust retrieval performance.
For feature selection, we found that an FIR model with a smoothing factor can improve retrieval performance significantly. For feature matching,
a method similar to ``dilation operators'' used in image processing gives better and more robust retrieval performance than other methods.
Although fMRI has been widely studied, little attention has been paid to content-based (``content'' means the explicit or implicit cognitive process) retrieval of images despite the existence of databases equipped with textual description (fMRIDC). Content-based retrieval is potentially useful in discovering brain activation patterns, and in diagnoses by comparing observed patterns with those of known diseases, leading to clinical applications.
We conducted a comprehensive investigation of feature extraction and similarity measures used in several research communities (including information retrieval (IR), signal processing, and computer vision(CV)), to set up a content-based
retrieval framework for a large, heterogeneous database. We developed methods for both hypothesis-based (stimulus known) and hypothesis-free (stimulus unknown) schemes. For the former, we adapted and extended an adaptive Finite Impulse Response (FIR) Model to get a more robust estimation of the activation level of brain regions. We then relaxed the assumption that the brain responds as a linear time-invariant (LTI) system, by using a 4-parameter ordinary differential equation to model brain responses. We then evaluated a number of similarity measures used in IR and CV, such as Latent Semantic Indexing (LSI), TFIDF, and Mahalanobis distance, etc. For the latter, we used a heuristic to select independent components with low mean temporal frequency, and applied a maximum weight bipartite matching technique to integrate component-level similarity and give a more robust retrieval performance.
For feature selection, we found that an FIR model with a smoothing factor can improve retrieval performance significantly. For feature matching,
a method similar to ``dilation operators'' used in image processing gives better and more robust retrieval performance than other methods.
PhysicalDescription
Extent
xvii, 150 pages
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Note
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Ph.D.
Note
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Includes bibliographical references (p. 139-148).
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Bai
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Bing
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1974-
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author
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Bing Bai
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Paul
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Paul Kantor
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Littman
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Michael
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internal member
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Advisory Committee
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Michael Littman
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Pavlovic
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Vladimir
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internal member
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Advisory Committee
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Vladimir Pavlovic
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(type = personal)
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Shokoufandeh
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(type = given)
Ali
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(authority = RULIB)
outside member
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Advisory Committee
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Ali Shokoufandeh
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Rutgers University
Role
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degree grantor
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Graduate School - New Brunswick
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school
OriginInfo
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2007
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2007
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Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
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rucore19991600001
Genre
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ETD doctoral
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Rights
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The author owns the copyright to this work.
Copyright
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Copyright protected
Availability
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Open
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Bing Bai
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Affiliation
Rutgers University. Graduate School - New Brunswick
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Non-exclusive ETD license
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Author Agreement 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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