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Automated and computationally effici...

Rutgers The State University of New Jersey, School of Graduate Studies.

Automated and computationally efficient joint motion analysis using low quality fluoroscopy images.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Automated and computationally efficient joint motion analysis using low quality fluoroscopy images./
作者:	Ghafurian, Soheil.
面頁冊數:	1 online resource (103 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
Contained By:	Dissertation Abstracts International78-08B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9781369634440

Automated and computationally efficient joint motion analysis using low quality fluoroscopy images.
Ghafurian, Soheil.

Automated and computationally efficient joint motion analysis using low quality fluoroscopy images. - 1 online resource (103 pages)

Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.

Thesis (Ph.D.)--Rutgers The State University of New Jersey, School of Graduate Studies, 2017.

Includes bibliographical references

The 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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369634440Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Automated and computationally efficient joint motion analysis using low quality fluoroscopy images.
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100 1 $a Ghafurian, Soheil. $3 1193224
245 1 0 $a Automated and computationally efficient joint motion analysis using low quality fluoroscopy images.
264 0 $c 2017
300 $a 1 online resource (103 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
500 $a Adviser: Kang Li.
502 $a Thesis (Ph.D.)--Rutgers The State University of New Jersey, School of Graduate Studies, 2017.
504 $a Includes bibliographical references
520 $a The 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.
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Biomedical engineering. $3 588770
650 4 $a Industrial engineering. $3 679492
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0541
690 $a 0546
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Rutgers The State University of New Jersey, School of Graduate Studies. $b Graduate School - New Brunswick. $3 1193018
773 0 $t Dissertation Abstracts International $g 78-08B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10584674 $z click for full text (PQDT)

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