Extracting and Mathematical Identifying Form of Stationary Noise in X-ray Images

Material type: 文書・図像類

Author: Sugiura, Akihiroほか

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Publication date: 2014-04-16

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Note (General)：: Image noise may prevent proper diagnostic X-ray imaging. This study is aimed at developing new noise rejection methods using a mathematical model that...

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Material Type: 文書・図像類
Title: Extracting and Mathematical Identifying Form of Stationary Noise in X-ray Images
Author/Editor: Sugiura, Akihiro
Yokoyama, Kiyoko
Takada, Hiroki
Ihori, Akiko
Yasuda, Naruomi
Yoshida, Takahiro
Author Heading: Sugiura, Akihiro
Yokoyama, Kiyoko
Takada, Hiroki
Ihori, Akiko
Yasuda, Naruomi
Yoshida, Takahiro
Publication Date: 2014-04-16
Publication Date (W3CDTF): 2014-04-16
Periodical title: Forma
No. or year of volume/issue: 29
Volume: 29
Pages: 37-43
Text Language Code: eng
Subject Heading: X-ray Image
Nonstochastic Noise
Stationary Noise
Polynomial Regression Model
CR System
Target Audience: 一般
Note (General): Image noise may prevent proper diagnostic X-ray imaging. This study is aimed at developing new noise rejection methods using a mathematical model that describes the form of X-ray image noise. Stationary noise is one type of noise found in X-ray images. Stationary noise is nonstochastic and appears independent of the radiographic factors. In this paper, we verify methods for identifying stationary noise using a polynomial regression model, and extracting such noise from X-ray images obtained from a CR system. The results of this study demonstrate that stationary noise can be extracted with high precision using a particular low-pass filter frequency. We found that a regression model for greater than second-degree polynomials can be applied for roughly identifying stationary noise. However, the fitting accuracy of the regression curve is not significantly improved in terms of the amount of multiplication required when increasing the degree of the polynomial regression model.
Access Restrictions: 限定公開
Rights (production): Copyright : Society for Science on Form, Japan.
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 福井大学 : 福井大学学術機関リポジトリ