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

Motohiro Tabuchi; Nobumoto Yamane; Yoshitaka Morikawa

doi:10.1109/SICE.2007.4421069

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 proceeding › Conference contribution

8 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 language	English
Title of host publication	SICE Annual Conference, SICE 2007
Pages	682-689
Number of pages	8
DOIs	https://doi.org/10.1109/SICE.2007.4421069
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/Territory	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

Access to Document

10.1109/SICE.2007.4421069

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., 4421069, Proceedings of the SICE Annual Conference, pp. 682-689, SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007, Takamatsu, Japan, 9/17/07. https://doi.org/10.1109/SICE.2007.4421069

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

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.",

keywords = "Adaptive wiener filter, Expectation-maximization algorithm, Gaussian mixture distribution model, Maximum a posteriori probability, Phantom",

author = "Motohiro Tabuchi and Nobumoto Yamane and Yoshitaka Morikawa",

year = "2007",

doi = "10.1109/SICE.2007.4421069",

language = "English",

isbn = "4907764286",

series = "Proceedings of the SICE Annual Conference",

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AU - Tabuchi, Motohiro

AU - Yamane, Nobumoto

AU - Morikawa, Yoshitaka

PY - 2007

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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

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

Abstract

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Other

Keywords

ASJC Scopus subject areas

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