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Model-based image segmentation in medical applications
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Title
Model-based image segmentation in medical applications
Name
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Qian
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Zhen
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Zhen Qian
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author
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Metaxas
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Dimitris
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Advisory Committee
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Dimitris N Metaxas
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chair
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Pavlovic
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Vladimir
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Advisory Committee
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Vladimir Pavlovic
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internal member
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Ahmed
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Advisory Committee
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Ahmed Elgammal
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Rutgers University
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Graduate School - New Brunswick
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theses
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2007
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2007
Language
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English
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electronic
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x, 60 pages
Abstract
Image segmentation is an essential and indispensable step in medical image analysis. It partitions the image into meaningful anatomic or pathological structures. Because medical image segmentation needs high level medical and anatomic knowledge, model-based segmentation methods are highly desirable. In this thesis, we will first give a short survey of current approaches of medical image segmentation. Then we specifically develop appearance and shape models for different segmentation tasks. These models are either obtained from visual observation and prior human expertise, or from certain automatic
machine learning methods. In this thesis, two model-based image segmentation algorithms are developed for 3D MR colonography and 2D cardiac tagged MRI. For 3D MR colonography, we manually build the shape and intensity model. For 2D tagged MRI, we learn the shape and local appearance model from a training set. In each application, besides the models, we give complete details in solving the segmentation problems, such as how we correct the MR image intensity inhomogeneity and how we automatically initialize the segmentation. Both model-based methods perform well on real medical image data.
machine learning methods. In this thesis, two model-based image segmentation algorithms are developed for 3D MR colonography and 2D cardiac tagged MRI. For 3D MR colonography, we manually build the shape and intensity model. For 2D tagged MRI, we learn the shape and local appearance model from a training set. In each application, besides the models, we give complete details in solving the segmentation problems, such as how we correct the MR image intensity inhomogeneity and how we automatically initialize the segmentation. Both model-based methods perform well on real medical image data.
Note
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M.S.
Note
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Includes bibliographical references (p. 58-60).
Subject
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Topic
Computer Science
Subject
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Topic
Diagnostic imaging
Subject
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(authority = ETD-LCSH)
Topic
Image processing
Subject
(ID = SUBJ4);
(authority = ETD-LCSH)
Topic
Magnetic resonance imaging
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.16760
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ETD_454
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NjNbRU
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doi:10.7282/T33N23SW
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ETD graduate
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Rights
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Copyright protected
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Open
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Name
Zhen Qian
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Affiliation
Rutgers University. Graduate School - New Brunswick
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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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