Adaptive Wiener filter based on Gaussian mixture distribution model for denoising chest X-ray CT image

Motohiro Tabuchi, Nobumoto Yamane, Yoshitaka Morikawa

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)563-572
Number of pages10
JournalNippon Hoshasen Gijutsu Gakkai zasshi
Volume64
Issue number5
DOIs
Publication statusPublished - May 20 2008

ASJC Scopus subject areas

  • Medicine(all)

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