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

Motohiro Tabuchi, Nobumoto Yamane, Yoshitaka Morikawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

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 languageEnglish
Title of host publicationSICE Annual Conference, SICE 2007
Pages682-689
Number of pages8
DOIs
Publication statusPublished - 2007
EventSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan
Duration: Sep 17 2007Sep 20 2007

Publication series

NameProceedings of the SICE Annual Conference

Other

OtherSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
CountryJapan
CityTakamatsu
Period9/17/079/20/07

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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    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