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Real-time imaging and automated segmentation of cartilage from 2D ultrasound images

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TitleInfo
Title
Real-time imaging and automated segmentation of cartilage from 2D ultrasound images
Name (type = personal)
NamePart (type = family)
Desai
NamePart (type = given)
Prajna Ramesh
NamePart (type = date)
1992-
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Prajna Ramesh Desai
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RoleTerm (authority = RULIB)
author
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Ilker
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Ilker Hacihaliloglu
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Advisory Committee
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chair
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Pierce
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Mark Pierce
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Advisory Committee
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internal member
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Boustany
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Nada
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Nada Boustany
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Advisory Committee
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internal member
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Rutgers University
Role
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degree grantor
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School of Graduate Studies
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theses
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2018-10
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2018
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2018
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English
Abstract (type = abstract)
Knee osteoarthritis (OA), a chronic joint condition, occurs when the cartilage cushion between the knee joints degrades over the age. The progression of OA is determined based on the cartilage degradation, therefore, cartilage thickness is an important measure for diagnosis and classication of OA. Currently, magnetic resonance imaging (MRI) is used as the gold standard imaging modality for the diagnosis of OA. However, the routine clinical monitoring using MRI is limited as MRI is expensive, has high scanning time, and limited accessibility. Recently, ultrasound (US) has shown its sensitivity to evaluate the cartilage changes. US provides cost-effective, and real time imaging of knee joint thus making it a potential alternative to MRI for routine clinical monitoring of cartilage degeneration. However, low contrast, speckle noise, signal attenuation, shadow artifacts, and being a user dependent imaging modality have prohibited the widespread use of this imaging modality for diagnosing OA. Various studies have been conducted to show the potential of US for routine clinical monitoring of OA. However, the studies were only focused on qualitative assessment of US cartilage image and the cartilage thickness was computed manually. This thesis, presents a fully automated cartilage segmentation and thickness computation from enhanced 2D US knee cartilage images.

The proposed framework consists of: (1) cartilage image enhancement, (2) bone surface segmentation for seed initialization, (3) seed-based cartilage segmentation, and (4) automatic cartilage thickness measurement. Local phase image features, by designing various bandpass quadrature lters, are extracted for enhancing the cartilage image, and bone surfaces. The segmentation of enhanced bone surfaces is achieved using a dynamic programming approach. The extracted bone surfaces are marked as an initial seeds for region based segmentation algorithms. Cartilage segmentation is evaluated using random walker (RW), watershed, and graph-cut methods. During the final step the segmented cartilage regions are used to compute mean cartilage thickness. The qualitative and quantitative validations are performed on 200 2D scans obtained from ten healthy volunteers. Validation against expert manual segmentation achieved mean dice similarity coecient (DSC) of 0.90, 0.86, and 0.84 for RW, watershed, and graph-cut respectively. The computed mean cartilage thickness ranged from 1.90 to 5.66 mm with the average value of 2.95 0.66mm. The Bland-Altman plots are used to compare the mean thickness error among dierent type of segmentation algorithm. This study presents a, fully automated US cartilage segmentation approach for cartilage segmentation. Presented results show the potential of US for imaging cartilage. The work will be invaluable for all future studies investigating US as an alternative imaging modality in OA research with a specic focus on cartilage.
Subject (authority = RUETD)
Topic
Biomedical Engineering
Subject (authority = ETD-LCSH)
Topic
Osteoarthritis -- Imaging
Subject (authority = ETD-LCSH)
Topic
Knee -- Diseases -- Imaging
RelatedItem (type = host)
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_9240
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1 online resource (xi, 71 pages) : illustrations
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M.S.
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Includes bibliographical references
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School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-1nv7-xk22
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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
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Desai
GivenName
Prajna
MiddleName
Ramesh
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2018-09-25 11:30:08
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Name
Prajna Desai
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Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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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.
Copyright
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Copyright protected
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Status
Open
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Permission or license
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2018-09-25T19:06:52
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2018-09-25T19:06:52
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