Adaptive Filter for Serial Slice Images of Cone-Beam Computed Tomography

Abstract

According to the noise properties and the serial slice image characteristics in cone-beam computed tomography (CBCT) system, an adaptive filter for serial slice images of CBCT was proposed. The judging criterion for the noise is established firstly. All pixels are classified into two classes, one is the pixels which are corrupted by Gauss noise and the other is the pixels corrupted by impulse noise. Then adaptive center weighted modified trimmed mean (ACWMTM) filter is used for the pixels corrupted by Gauss noise and adaptive median (AM) filter is used for the pixels corrupted by impulse noise. In ACWMTM filtering algorithm, the estimated Gauss noise standard deviation in the current slice image with offset window is replaced by the estimated standard deviation in the adjacent slice image to the current with the corresponding window, so the filtering accuracy of the serial images is improved. The filtering experiments on CBCT serial slice images of wax model of hollow turbine blade show that the algorithm combines the advantages of ACWMTM filtering algorithm and AM filtering algorithm, and makes a good performance both on eliminating noises and on protecting details.