CT Image Noise Reduction Based on Adaptive Wiener Filtering with Wavelet Packet Thresholding

Computed Tomography (CT) is streamlined in radiological diagnostics and has become an imperative tool in medical examinations. The difficulty that arises with the demand is to improve CT image quality without increasing dose. In this paper, Wavelet based noise reduction technique is proposed to improve image quality where adaptive Wiener filtering and Wavelet Packet Threshold (WPT) algorithm are applied. The Noisy CT image is decomposed using DWT, where approximation part is filtered using WPT algorithm and detail part is filtered by the adaptive Wiener ltering. By using the level dependent, the wavelet packet tree coefficients are calculated using optimal linear interpolation shrinkage function. Denoised image is acquired using wavelet packet reconstruction and inverse DWT. The value of the peak signal to noise ratio (PSNR) is used as the measure of image visual quality. Experimental results demonstrate that the proposed method improves the image visual quality in respect of noise removal and edge preservation.