Abstract
Because the X-ray CT imaging has high spatial resolution, it becomes more important in diagnostic imaging. However the techniques of low dose imaging at X-ray mass examination or thin slice imaging provide degraded CT images by noise. The CT images have specific noise, called streak artifact. In this paper, we apply an adaptive Wiener filter (AWF) based on the Gaussian mixture distribution model (GMM), proposed previously to reduce Gaussian white noise. Simulation results show that a new AWF-GMM designed using high dose (original) CT image and low dose (observed) CT image pairs of chest phantom for training image set provides high restoration ability.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the SICE Annual Conference |
| Pages | 682-689 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 2007 |
| Event | SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan Duration: Sep 17 2007 → Sep 20 2007 |
Other
| Other | SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 |
|---|---|
| Country | Japan |
| City | Takamatsu |
| Period | 9/17/07 → 9/20/07 |
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Keywords
- Adaptive wiener filter
- Expectation-maximization algorithm
- Gaussian mixture distribution model
- Maximum a posteriori probability
- Phantom
ASJC Scopus subject areas
- Engineering(all)
Cite this
Adaptive Wiener filter based on Gaussian mixture model for denoising chest X-ray CT image. / Tabuchi, Motohiro; Yamane, Nobumoto; Morikawa, Yoshitaka.
Proceedings of the SICE Annual Conference. 2007. p. 682-689 4421069.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Adaptive Wiener filter based on Gaussian mixture model for denoising chest X-ray CT image
AU - Tabuchi, Motohiro
AU - Yamane, Nobumoto
AU - Morikawa, Yoshitaka
PY - 2007
Y1 - 2007
N2 - Because the X-ray CT imaging has high spatial resolution, it becomes more important in diagnostic imaging. However the techniques of low dose imaging at X-ray mass examination or thin slice imaging provide degraded CT images by noise. The CT images have specific noise, called streak artifact. In this paper, we apply an adaptive Wiener filter (AWF) based on the Gaussian mixture distribution model (GMM), proposed previously to reduce Gaussian white noise. Simulation results show that a new AWF-GMM designed using high dose (original) CT image and low dose (observed) CT image pairs of chest phantom for training image set provides high restoration ability.
AB - Because the X-ray CT imaging has high spatial resolution, it becomes more important in diagnostic imaging. However the techniques of low dose imaging at X-ray mass examination or thin slice imaging provide degraded CT images by noise. The CT images have specific noise, called streak artifact. In this paper, we apply an adaptive Wiener filter (AWF) based on the Gaussian mixture distribution model (GMM), proposed previously to reduce Gaussian white noise. Simulation results show that a new AWF-GMM designed using high dose (original) CT image and low dose (observed) CT image pairs of chest phantom for training image set provides high restoration ability.
KW - Adaptive wiener filter
KW - Expectation-maximization algorithm
KW - Gaussian mixture distribution model
KW - Maximum a posteriori probability
KW - Phantom
UR - http://www.scopus.com/inward/record.url?scp=50249153697&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=50249153697&partnerID=8YFLogxK
U2 - 10.1109/SICE.2007.4421069
DO - 10.1109/SICE.2007.4421069
M3 - Conference contribution
AN - SCOPUS:50249153697
SN - 4907764286
SN - 9784907764289
SP - 682
EP - 689
BT - Proceedings of the SICE Annual Conference
ER -