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dc.contributor.advisorMohamed, Feroze B.
dc.creatorKayal, Nilanjan
dc.date.accessioned2020-10-26T19:19:45Z
dc.date.available2020-10-26T19:19:45Z
dc.date.issued2012
dc.identifier.other864885934
dc.identifier.urihttp://hdl.handle.net/20.500.12613/1584
dc.description.abstractIn spinal cord injury the amount of total surviving white matter is known to be strongly related to post injury neurological functions (1). Accurate segmentation of these regions is shown to be critical in terms of developing effective treatment (1). Diffusion Tensor Imaging (DTI) has been shown to be effective in obtaining spinal cord images (2). However challenges still exist in clear separation of gray/white/cerebrospinal fluid (CSF) structures within the cord using DTI. The purpose of this study is to (1) test a semi-automatic tissue segmentation algorithm based on grow cut algorithm (GCA), to classify CSF, gray and white matter in conventional T2 weighted MRI and Diffusion Tensor Imaging (DTI) images in pediatric spinal cord injury (SCI) subjects, and (2) to compare the results of semi-automatic GCA segmentation with manually segmented spinal cord data performed on various DTI images by a board certified pediatric neuroradiologist. Results show that semi-automatic segmentation of the spinal cord using GCA was successfully implemented. Qualitatively, good separation of cord/CSF was seen in B0, CFA and FA maps (of a representative patient with SCI and a control using this GCA method. They demonstrate more homogeneous signal within the cervical spinal cord as well as greater conspicuity of the cord and surrounding CSF interface. Quantitative analysis of images segmented using GCA and manual segmentation between and within the groups showed no significant differences in CFA (p=0.1347) and FA (p=0.1442) images but B0 (p=0.0001) images showed statistically significant differences. Overall, in both the controls and subjects with SCI, quantitative and qualitative analysis showed a superior semi-automated segmentation on CFA and FA images over a B0 image the using modified GCA. Key words: Grow Cut Algorithm (GCA), Magnetic Resonance Imaging (MRI), segmentation, Diffusion Tensor Imaging (DTI), cervical spinal cord, cerebral-spinal fluid (CSF).
dc.format.extent49 pages
dc.language.isoeng
dc.publisherTemple University. Libraries
dc.relation.ispartofTheses and Dissertations
dc.rightsIN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available.
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectEngineering, Biomedical
dc.subjectCervical Spinal Cord
dc.subjectDiffusion Tensor Imaging
dc.subjectGrow Cut Algorithm (gca)
dc.subjectMagnetic Resonance Imaging (mri)
dc.subjectSegmentation
dc.titleAN INVESTIGATION OF GROW CUT ALGORITHM FOR SEGMENTATION OF MRI SPINAL CORD IMAGES IN NORMALS AND PATIENTS WITH SCI
dc.typeText
dc.type.genreThesis/Dissertation
dc.contributor.committeememberFaro, Scott H.
dc.contributor.committeememberPleshko, Nancy
dc.description.departmentBioengineering
dc.relation.doihttp://dx.doi.org/10.34944/dspace/1566
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.description.degreeM.S.
refterms.dateFOA2020-10-26T19:19:45Z


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