A New Technique Based on AMID Using Adaptive Thresholding for Ultrasound Speckle Reduction

Abstract

A new technique based on adaptive multi-resolution image decomposition (AMID) using adaptive thresholding for ultrasound speckle reduction has been proposed in this paper. The noisy image is first filtered to reduce large multiplicative noise. The image is then log-transformed before decomposing it to its approximation and detailed coefficients using AMID algorithm. The approximation coefficients are appropriately filtered while adaptive thresholding is applied to the detail coefficients. The image is reconstructed back from all the modified coefficients to obtain the denoised image. This method combining adaptive decomposition with adaptive thresholding makes it adaptive to both signal and noise components, thereby retaining the edge details in the denoised image effectively. The proposed technique performance is tested on synthetic as well as real ultrasound images with the noise of different variances. The experimental results show that the proposed algorithm gives better performance than the other state-of-the-art methods in terms of edge keeping index (EKI), correlation coefficient (CC), figure of merit (FOM), peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and signal-to-noise ratio (SNR) for synthetic images. The algorithm gives better performance in terms of equivalent number of looks (ENL) and mean-to-variance ratio (MVR) for real ultrasound images.