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dc.contributor.authorEntrekin, Dean Allenen_US
dc.date.accessioned2011-08-06T16:01:39Z
dc.date.available2011-08-06T16:01:39Z
dc.date.issued2004-02-10en_US
dc.identifier.otheretd-05262004-144020en_US
dc.identifier.urihttp://hdl.handle.net/10919/9957
dc.description.abstractCurrent methods for treating and diagnosing spinal deformities caused by scoliosis are both surgically intensive and rarely allow for complete correction. This is mainly due to the fact that the diagnostic techniques used are rough estimates made by angles defined by observations of 2-D radiographs. By utilizing the latest software, our research is based on designing a tool that creates a 3-D representation of the spine. When creating a three-dimensional spinal model, it becomes possible to determine local curvature and local torsion values at each specific vertebrae. By manipulating these values at discrete locations on the spine, one can generate "virtual" spines in a three-dimensional environment. The Scoliosis Learning Tool includes algorithmic steps that determine the Lenke Classification of the "virtual" spines. The Lenke Classification is the most commonly accepted method for diagnosing spinal deformities. This patient building program will produce a group of spines with random values for curvature, torsion and initial spinal orientation. An algorithm within the software determines the Lenke Classification of each, and discards any curves that appear unnatural. By defining a metric that places an emphasis on certain geometric similarities, the software is able to define diameters of classification groups and separations between different classification groups. In turn it is possible to determine minor to major differences between spines within the same classification. In doing so, the opportunity exists to possibly find an undiscovered deformity that had previously fallen under another classification category.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.relation.haspartThesis_DeanEntrekin.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectthree dimensional spinal deformityen_US
dc.subjectscoliosisen_US
dc.subjectLenke Classificationen_US
dc.titleOn the Geometric Characterization of the Lenke Classification Scheme for Idiopathic Scoliosisen_US
dc.typeThesisen_US
dc.contributor.departmentBiomedical Engineering and Sciencesen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineBiomedical Engineering and Sciencesen_US
dc.contributor.committeechairDankowicz, Harry J.en_US
dc.contributor.committeememberMadigan, Michael L.en_US
dc.contributor.committeememberShilt, Jeffen_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05262004-144020en_US
dc.date.sdate2004-05-26en_US
dc.date.rdate2004-06-10
dc.date.adate2004-06-10en_US


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