The University of Southampton
University of Southampton Institutional Repository

Automated tracking in digitized videofluroscopy sequences for spine kinematics analysis

Automated tracking in digitized videofluroscopy sequences for spine kinematics analysis
Automated tracking in digitized videofluroscopy sequences for spine kinematics analysis
Spine kinematic analysis provides useful information to aid understanding of the segmental motion of the vertebrae.
Digitized videofluoroscopy (DVF) is the existing practical modality to image spine motion for kinematic data acquisition. However, obtaining kinematic parameters from DVF sequence requires manual landmarking which is a laborious process and can be subjective and error prone.
This work develops an automated spine motion tracking algorithm for DVF sequences within a Bayesian framework. By utilizing the anatomical relationships between vertebrae, a dynamic Bayesian network with a particle filter at each node is constructed.
The proposed algorithm overcomes the dimensionality problem in a regular particle filter and has more efficient and robust performance. It can provide results of about 1° and 2 pixels View the MathML source variability in rotation and translation estimation, respectively, during repeated initialization analysis on sequences from simulation and in vivo healthy human subject studies.
Videofluoroscopy, Spine kinematics, Particle filter, Dynamic Bayesian network, Image processing
0262-8856
1555-1571
Lam, Shing Chun Benny
6334eb0f-23ef-45fc-8ec7-2eed49736a1f
McCane, Brendan
74aacd9a-2301-4649-be0e-b6d3966e27c2
Allen, Robert
956a918f-278c-48ef-8e19-65aa463f199a
Lam, Shing Chun Benny
6334eb0f-23ef-45fc-8ec7-2eed49736a1f
McCane, Brendan
74aacd9a-2301-4649-be0e-b6d3966e27c2
Allen, Robert
956a918f-278c-48ef-8e19-65aa463f199a

Lam, Shing Chun Benny, McCane, Brendan and Allen, Robert (2009) Automated tracking in digitized videofluroscopy sequences for spine kinematics analysis. Image and Vision Computing, 27 (10), 1555-1571. (doi:10.1016/j.imavis.2009.02.010).

Record type: Article

Abstract

Spine kinematic analysis provides useful information to aid understanding of the segmental motion of the vertebrae.
Digitized videofluoroscopy (DVF) is the existing practical modality to image spine motion for kinematic data acquisition. However, obtaining kinematic parameters from DVF sequence requires manual landmarking which is a laborious process and can be subjective and error prone.
This work develops an automated spine motion tracking algorithm for DVF sequences within a Bayesian framework. By utilizing the anatomical relationships between vertebrae, a dynamic Bayesian network with a particle filter at each node is constructed.
The proposed algorithm overcomes the dimensionality problem in a regular particle filter and has more efficient and robust performance. It can provide results of about 1° and 2 pixels View the MathML source variability in rotation and translation estimation, respectively, during repeated initialization analysis on sequences from simulation and in vivo healthy human subject studies.

This record has no associated files available for download.

More information

Published date: 2 September 2009
Keywords: Videofluoroscopy, Spine kinematics, Particle filter, Dynamic Bayesian network, Image processing

Identifiers

Local EPrints ID: 79168
URI: http://eprints.soton.ac.uk/id/eprint/79168
ISSN: 0262-8856
PURE UUID: 8545c19a-b336-4231-870d-cc8dd59ad323

Catalogue record

Date deposited: 15 Mar 2010
Last modified: 14 Mar 2024 00:28

Export record

Altmetrics

Contributors

Author: Shing Chun Benny Lam
Author: Brendan McCane
Author: Robert Allen

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×