Using Non-Sub sampled Shearlet Transform and Nakagami Model for Ultrasound Image De-Speckling

Abstract

<em>Ultrasound images suffer of multiplicative noise named speckle. Different de-speckling algorithms run either in spatial domain or in transformed domain. </em><em>In this paper, an adaptive filter in spatial domain according to assume the Nakagami distribution as the statistic of log-compressed ultrasound images is used. For de-speckling in transformed domain, the non-sub sampled shearlet transform is used. In addition, the Bayesian shrinkage as a well-known method for finding the optimum threshold values in transformed domain is applied. The main contribution of this paper is comparing the performance of two methods that suppress the speckle noise in spatial domain and transformed domain. For this purpose, a synthetic test image and the original ultrasound images are processed and peak signal to noise ratio (PSNR), mean square error (MSE), structural similarity (SSIM), edge keeping index (EKF), noise variance (NV), mean square difference (MSD), and equivalent number of looks (ENL) are obtained.</em>