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Noise reduction in fluroscopic image sequences for joint kinematics analysis

Noise reduction in fluroscopic image sequences for joint kinematics analysis
Noise reduction in fluroscopic image sequences for joint kinematics analysis
Analysis of dynamic videofluoroscopic can provide spine kinematic data with an acceptable low X-ray dose. Estimation of the kinematics relies on accurate recognition of vertebrae positions and rotations on each radiological frame. In previous works we presented a procedure for automatic tracking of vertebra motion by smoothed gradient operators and template matching in fluoroscopic image sequences. A limitation to the accurate estimation of the kinematics by automatic tracking of vertebrae motion, independently by the specific methodology employed (e.g. manual marking, corner or edge automatic detection, etc.), is mainly due to noise: low-dose X-ray image sequences exhibit severe signal-dependent noise that should be reduced, while preserving anatomical edges and structures. Noise in low-dose X-ray images originates from various sources, however quantum noise is by far the more dominant noise in low-dose X-ray images and other sources can be neglected. Signal degraded by quantum noise is commonly modeled by a Poisson distribution, but it is possible to approximate it as additive zero-mean Gaussian noise with signal-dependent variance. In this work we propose a digital spatial filter for reducing noise in low-dose X-ray images. The proposed filter is based on averaging of only similar pixels (whose grey level is contained within ±3) instead of spatial averaging of all neighbouring pixels. The effectiveness of the filter performance was evaluated by fluoroscopic image sequence processing, comparing the results of the automatic vertebra tracking on filtered and unfiltered images
9783642130380
323-326
Springer
Cerciello, T.
f3bb999d-7f40-45be-b6c1-b8f721d3c02c
Bifulco, P.
1d59ed38-1ce6-4798-b117-d22712198dda
Cesarelli, M.
1b5a9e20-2a43-474a-b163-77409a10df2f
Paura, L.
7ab9560d-54c6-48b2-81af-dc739b7c56fe
Pasquariello, G.
1d87a642-1096-454a-85d2-821e9f8d7401
Allen, R.
956a918f-278c-48ef-8e19-65aa463f199a
Bamidis, Panagiotis D.
Pallikarakis, Nicolas
Cerciello, T.
f3bb999d-7f40-45be-b6c1-b8f721d3c02c
Bifulco, P.
1d59ed38-1ce6-4798-b117-d22712198dda
Cesarelli, M.
1b5a9e20-2a43-474a-b163-77409a10df2f
Paura, L.
7ab9560d-54c6-48b2-81af-dc739b7c56fe
Pasquariello, G.
1d87a642-1096-454a-85d2-821e9f8d7401
Allen, R.
956a918f-278c-48ef-8e19-65aa463f199a
Bamidis, Panagiotis D.
Pallikarakis, Nicolas

Cerciello, T., Bifulco, P., Cesarelli, M., Paura, L., Pasquariello, G. and Allen, R. (2010) Noise reduction in fluroscopic image sequences for joint kinematics analysis. Bamidis, Panagiotis D. and Pallikarakis, Nicolas (eds.) In Proceedings of the 22nd Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2010. vol. 29, Springer. pp. 323-326 .

Record type: Conference or Workshop Item (Paper)

Abstract

Analysis of dynamic videofluoroscopic can provide spine kinematic data with an acceptable low X-ray dose. Estimation of the kinematics relies on accurate recognition of vertebrae positions and rotations on each radiological frame. In previous works we presented a procedure for automatic tracking of vertebra motion by smoothed gradient operators and template matching in fluoroscopic image sequences. A limitation to the accurate estimation of the kinematics by automatic tracking of vertebrae motion, independently by the specific methodology employed (e.g. manual marking, corner or edge automatic detection, etc.), is mainly due to noise: low-dose X-ray image sequences exhibit severe signal-dependent noise that should be reduced, while preserving anatomical edges and structures. Noise in low-dose X-ray images originates from various sources, however quantum noise is by far the more dominant noise in low-dose X-ray images and other sources can be neglected. Signal degraded by quantum noise is commonly modeled by a Poisson distribution, but it is possible to approximate it as additive zero-mean Gaussian noise with signal-dependent variance. In this work we propose a digital spatial filter for reducing noise in low-dose X-ray images. The proposed filter is based on averaging of only similar pixels (whose grey level is contained within ±3) instead of spatial averaging of all neighbouring pixels. The effectiveness of the filter performance was evaluated by fluoroscopic image sequence processing, comparing the results of the automatic vertebra tracking on filtered and unfiltered images

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Published date: May 2010

Identifiers

Local EPrints ID: 158183
URI: https://eprints.soton.ac.uk/id/eprint/158183
ISBN: 9783642130380
PURE UUID: d570a97b-cffa-4aab-ab1d-c018e9624ae1

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Date deposited: 17 Jun 2010 13:00
Last modified: 18 Jul 2017 12:39

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