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Conference Paper: Automatic lumbar motion analysis based on particle filtering

TitleAutomatic lumbar motion analysis based on particle filtering
Authors
KeywordsParticle Filter
Lumbar Spine
Vertebral Body
Issue Date2012
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800121
Citation
The 2012 IEEE International Conference on System Science and Engineering (ICSSE), Dalian, Liaoning, China, 30 June-2 July 2012. In ICSSE Proceedings, 2012, p. 60-63 How to Cite?
AbstractSpinal motion is produced by complex coordination of nerves and muscles and is constrained by vertebral structure. The observation and measurement of lumbar motion is of great value for clinical diagnosis and surgical plan of lumbar disorders. Digitalized Video Fluoroscopy (DVF) is the most suitable one to image the spine motion but it is quite time consuming. This paper proposes an automatic lumbar motion analysis system (ALMAS) with particle filtering technology. The automatically vertebral tracking for motion analysis was utilized with a friendly-interface, which provides a window for users to process the acquired DVF sequence and to analyze the tracking results. A set of simulation vertebra image were used to evaluate the performance and accuracy of this system. In simulated sequence, the maximal difference is 1.3 mm in translation and 1ͦ in rotation angle. The error is small in x- and y- translation (fiducial error: 2.4%, repeatability error: 0.5%) and in rotation angle (fiducial error: 1.0%, repeatability error: 0.7%). The ALMAS can still track the sequence contaminated by noise with the density ≤ 0.5. Besides, the results demonstrate that the data from the auto-tracking algorithm shows a strong correlation with the actual measurement and that the ALMAS is highly repetitive. Results from this study showed that ALMAS based on particle filtering are relatively robust and accurate for automatic lumbar motion analysis.
Persistent Identifierhttp://hdl.handle.net/10722/181796
ISBN

 

DC FieldValueLanguage
dc.contributor.authorXu, Men_US
dc.contributor.authorZhang, Yen_US
dc.contributor.authorXie, Xen_US
dc.contributor.authorCui, Hen_US
dc.contributor.authorXu, Sen_US
dc.contributor.authorHu, Yen_US
dc.date.accessioned2013-03-19T03:58:16Z-
dc.date.available2013-03-19T03:58:16Z-
dc.date.issued2012en_US
dc.identifier.citationThe 2012 IEEE International Conference on System Science and Engineering (ICSSE), Dalian, Liaoning, China, 30 June-2 July 2012. In ICSSE Proceedings, 2012, p. 60-63en_US
dc.identifier.isbn978-1-4673-0945-5-
dc.identifier.urihttp://hdl.handle.net/10722/181796-
dc.description.abstractSpinal motion is produced by complex coordination of nerves and muscles and is constrained by vertebral structure. The observation and measurement of lumbar motion is of great value for clinical diagnosis and surgical plan of lumbar disorders. Digitalized Video Fluoroscopy (DVF) is the most suitable one to image the spine motion but it is quite time consuming. This paper proposes an automatic lumbar motion analysis system (ALMAS) with particle filtering technology. The automatically vertebral tracking for motion analysis was utilized with a friendly-interface, which provides a window for users to process the acquired DVF sequence and to analyze the tracking results. A set of simulation vertebra image were used to evaluate the performance and accuracy of this system. In simulated sequence, the maximal difference is 1.3 mm in translation and 1ͦ in rotation angle. The error is small in x- and y- translation (fiducial error: 2.4%, repeatability error: 0.5%) and in rotation angle (fiducial error: 1.0%, repeatability error: 0.7%). The ALMAS can still track the sequence contaminated by noise with the density ≤ 0.5. Besides, the results demonstrate that the data from the auto-tracking algorithm shows a strong correlation with the actual measurement and that the ALMAS is highly repetitive. Results from this study showed that ALMAS based on particle filtering are relatively robust and accurate for automatic lumbar motion analysis.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800121-
dc.relation.ispartofInternational Conference on System Science and Engineering Proceedingsen_US
dc.rightsInternational Conference on System Science and Engineering Proceedings. Copyright © IEEE.-
dc.rights©2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectParticle Filter-
dc.subjectLumbar Spine-
dc.subjectVertebral Body-
dc.titleAutomatic lumbar motion analysis based on particle filteringen_US
dc.typeConference_Paperen_US
dc.identifier.emailHu, Y: yhud@hku.hken_US
dc.identifier.authorityHu, Y=rp00432en_US
dc.description.naturepublished_or_final_version-
dc.identifier.scopuseid_2-s2.0-84866647470-
dc.identifier.hkuros213621en_US
dc.identifier.spage60-
dc.identifier.epage63-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 130418-

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