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

Ultrasound images suffer of multiplicative noise named speckle. Different de-speckling algorithms run either in spatial domain or in transformed domain. In thispaper, an adaptive filter in spatial domain according to assume the Nakagamidistribution as the statistic of log-ompressed ultrasound images is used. For de-speckling in transformed domain, the non-sub sampled shearlet transform is used. Inaddition, the Bayesian shrinkage as a well-known method for finding the optimumthreshold values in transformed domain is applied. The main contribution of thispaper is comparing the performance of two methods that suppress the speckle noisein spatial domain and transformed domain. For this purpose, a synthetic test Imageand 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 numberof looks (ENL) are obtained.

Keywords:

Nakagami distribution , adaptive filter , non sub-sampled shearlet transform , ultrasound image de-speckling , Bayesian shrinkage thresholding