Adaptive Wiener filter based on Gaussian mixture distribution model for denoising chest X-ray CT image
In recent decades, X-ray CT imaging has become more important as a result of its high-resolution performance. However, it is well known that the X-ray dose is insufficient in the techniques that use low-dose imaging in health screening or thin-slice imaging in work-up. Therefore, the degradation of...
Gespeichert in:
| Veröffentlicht in: | Nippon Hōshasen Gijutsu Gakkai zasshi Jg. 64; H. 5; S. 563 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Japanisch |
| Veröffentlicht: |
Japan
20.05.2008
|
| Schlagworte: | |
| ISSN: | 0369-4305 |
| Online-Zugang: | Weitere Angaben |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Zusammenfassung: | In recent decades, X-ray CT imaging has become more important as a result of its high-resolution performance. However, it is well known that the X-ray dose is insufficient in the techniques that use low-dose imaging in health screening or thin-slice imaging in work-up. Therefore, the degradation of CT images caused by the streak artifact frequently becomes problematic. In this study, we applied a Wiener filter (WF) using the universal Gaussian mixture distribution model (UNI-GMM) as a statistical model to remove streak artifact. In designing the WF, it is necessary to estimate the statistical model and the precise co-variances of the original image. In the proposed method, we obtained a variety of chest X-ray CT images using a phantom simulating a chest organ, and we estimated the statistical information using the images for training. The results of simulation showed that it is possible to fit the UNI-GMM to the chest X-ray CT images and reduce the specific noise. |
|---|---|
| Bibliographie: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
| ISSN: | 0369-4305 |
| DOI: | 10.6009/jjrt.64.563 |