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 | SICE Annual Conference, SICE 2007 |
| 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 |
Publication series
| Name | Proceedings of the SICE Annual Conference |
|---|
Other
| Other | SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 |
|---|---|
| Country | Japan |
| City | Takamatsu |
| Period | 9/17/07 → 9/20/07 |
Keywords
- Adaptive wiener filter
- Expectation-maximization algorithm
- Gaussian mixture distribution model
- Maximum a posteriori probability
- Phantom
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering
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Cite this
Tabuchi, M., Yamane, N., & Morikawa, Y. (2007). Adaptive Wiener filter based on Gaussian mixture model for denoising chest X-ray CT image. In SICE Annual Conference, SICE 2007 (pp. 682-689). [4421069] (Proceedings of the SICE Annual Conference). https://doi.org/10.1109/SICE.2007.4421069