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Automated and computationally efficient joint motion analysis using low quality fluoroscopy images
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Ghafurian, Soheil. Automated and computationally efficient joint motion analysis using low quality fluoroscopy images. Retrieved from https://doi.org/doi:10.7282/T3S46VDM

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TitleAutomated and computationally efficient joint motion analysis using low quality fluoroscopy images
NameGhafurian, Soheil (author); Li, Kang (chair); Albin, Susan (internal member); Jeong, Myong K. (internal member); Hacihaliloglu, Ilker (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2017
Other Date2017-01 (degree)
SubjectIndustrial and Systems Engineering, Joints--Physiology, Fluoroscopy
Extent1 online resource (xviii, 85 p. : ill.)
DescriptionThe kinematic analysis of joint motion has proven to significantly improve orthopedic surgeries by enhancing surgery assessment, prosthesis design, and pathology diagnosis. This analysis is usually performed by a three to two dimensional (3D/2D) registration of the 3D bone model to a 2D radiographic video from C-arm fluoroscopy imaging machines. However, the practicality of such analysis is undermined due to lengthy and user-dependent 3D/2D image registration algorithms and the high cost of C-arm fluoroscopy imaging machines. Mini C-arm fluoroscopic machines are a more affordable alternative, but the low quality of their images has hindered their use in this application. In this thesis, we propose a novel 3D/2D image registration algorithm for the kinematic analysis of joint bones using mini C-arm fluoroscopy machines with significantly improved speed, despite their low quality images. This method performs a fast registration through a novel algorithm for quick and high-quality generation of digitally reconstructed radiographs (DRR), which is the bottleneck in such processes. Moreover, the dependency of the results on the user has been reduced as a new feature-based registration algorithm replaced the previously manual initialization phase of the process. This algorithm is able to reach the true registration from within 90 degrees of it, which is a substantial improvement over the existing methods. In addition, our algorithm performs the registration in significantly reduced time due to a smaller number of generated DRRs.
NotePh.D.
NoteIncludes bibliographical references
Noteby Soheil Ghafurian
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3S46VDM
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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