Quantitative Comparison of Speckle Smoothing for Ultrasound Images Using Besov Norm

Mong-Shu      Lee; Mu-Yen      Chen; Cho-Li      Yen

doi:10.2174/1574893611308010006

Abstract

This paper presents a novel speckle smoothing method for ultrasound images. This smoothing method is designed to preserve both the edges and structural details of the image. Speckle noise is suppressed by extending the smoothness of the image in the wavelet-based Hölder spaces. We try to solve the performance of speckle reduction problem from the viewpoint of Besov norm. A comparison of smoothing speckles with the other well-known methods is provided via the size of Besov norm. We validate the proposed method using synthetic data, simulated and real ultrasound images. Experiments demonstrate the performance improvement of the proposed method over other state-of-the-art methods in terms of image quality and edge preservation indices.

Keywords: Function spaces, speckle reduction, ultrasound image, wavelet transforms, Assessment Parameters, Gaussian noise, multi-resolution analysis, Structural SIMilarity, despeckling methods, FOM