Despeckling of 3D ultrasound image using tensor low rank approximation

Abstract

In this paper, a new method for despeckling of 3D ultrasound images based on tensor low rank approximation technique is presented. The main motive of this paper is to establish a new denoising method which can incorporate the redundancy present in 3D ultrasound image in all directions unlike the existing techniques. Hence, a substantial improvement in the overall performance can be achieved by preserving the edges and other finer details of the image. In order to achieve this goal, the 3D ultrasound image is modeled in terms of low rank tensor approximation of grouped tensors which is solved by minimizing the Tensor Nuclear Norm (TNN) in tensor-Singular Value Decomposition (t-SVD) framework. The resulting optimization problem is solved by the method of inexact Augmented Lagrangian Multipliers (ALM). To the best of our understanding, this is the first attempt to model the 3D ultrasound image despeckling problem as tensor low rank recovery technique. The proposed algorithm is compared with popular state-of-art works in literature including a low rank based approach termed as DLRA [1] designed for 2D ultrasound images, and it is found that our scheme offers superior performance than its counterparts.