Akihiro Sugiura1,2*, Kiyoko Yokoyama1, Hiroki Takada3, Akiko Ihori2, Naruomi Yasuda2 and Takahiro Yoshida2
1Graduate School of Design and Architecture, Nagoya City University, 2-1-10 Kitachikusa, Chikusa-ku, Nagoya 464-0083, Japan
2Department of Radiological Technology, Gifu University of Medical Science, 795-1 Nagamine Ichihiraga, Seki, Gifu 501-3892, Japan
3Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui, Fukui 910-8507, Japan
*E-mail address: firstname.lastname@example.org
(Received November 14, 2010; Accepted April 9, 2011)
Abstract. 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.
Keywords: X-ray Image, Nonstochastic Noise, Stationary Noise, Polynomial Regression Model and CR System