CT image denoising based on complex wavelet transform using local adaptive thresholding and Bilateral filtering

Abstract

Computed Tomography (CT) is one of the most widespread radiological tools in medical diagnostics. To achieve good quality of CT images with low radiation dose has drawn a lot of attention to researchers. Hence, post processing of CT images has become a major concern in medical image processing. This paper presents a novel edge-preserving image denoising scheme based on Dual-tree Complex Wavelet Transform (DT-CWT), Bilateral filtering and a locally adaptive thresholding method. The noisy image is decomposed into Complex Wavelet coefficients through a Dual-tree Complex Wavelet Transform. Low-pass subbands are modified using Bilateral filter. High pass subbands are modified using locally adaptive thresholding based on interscale statistical dependency, where the noise variance of noisy wavelet coefficients are estimated using a robust median estimator. Denoised image is retrieved using inverse DT-CWT. The proposed scheme is compared with existing methods. It is observed that performance of proposed method is superior than existing methods in terms of visual quality, Image Quality Index (IQI) and Peak Signal-to-noise Ratio (PSNR).