Recursive Gauss-Seidel median filter for CT lung image denoising

Abstract

Poisson and Gaussian noises have been known to affect Computed Tomography (CT) image quality during reconstruction. Standard median (SM) Filter has been widely used to reduce the unwanted impulsive noises. However, it cannot perform satisfactorily once the noise density is high. Recursive median (RM) filter has also been proposed to optimize the denoising. On the other hand, the image quality is degraded. In this paper, we propose a hybrid recursive median (RGSM) filtering technique by using Gauss-Seidel Relaxation to enhance denoising and preserve image quality in RM filter. First, the SM filtering was performed, followed by Gauss-Seidel, and combined to generate secondary approximation solution. This scheme was iteratively done by applying the secondary approximation solution to the successive iterations. Progressive noise reduction was accomplished in every iterative stage. The last stage generated the final solution. Experiments on CT lung images show that the proposed technique has higher noise reduction improvements compared to the conventional RM filtering. The results have also confirmed better anatomical quality preservation. The proposed technique may improve lung nodules segmentation and characterization performance.