Enhancing 2D elasticity and viscosity images
A denoising approach for reflected and random noise removal
Introduction: Nowadays, Shear Wave Elastography (SWE) images are extensively employed for the early detection of various cancers including liver, breast, thyroid, and prostate cancers. This plays a pivotal role in cancer management. SWE images rely on estimating the Complex Shear Modulus (CSM), which is contingent upon two parameters of tissue properties in vivo: elasticity and viscosity.
Problem: Reflected noise, random noise, and speckle noise affect the quality of ultrasound images. Objective: Simulating the shear wave propagation by using Finite-Difference Time-Domain (FDTD) method, removing noises, and reconstructing the elasticity and viscosity images of tissues.
Methodology: In this study, we utilized several noise filters to eradicate unwanted noise. We employed a directional filter to eliminate reflected noise, an LMS filter to remove random noise, and a median filter for speckle noise removal. Subsequently, we applied the AHI algorithm to estimate the elasticity and viscosity of tissue.
Results: The elasticity and viscosity 2D images are estimated, underwent filtering resulting in improved images quality. Root Mean Square Error (RMSE) was used to evaluate the effectiveness of the estimation.
Conclusion: The obtained results include 2D images of tissue elasticity and viscosity, demonstrating the effectiveness of the filter in noise removal and AHI algorithm in estimating. Originality: We propose filters for noise removal (consist of reflected noise), before applying AHI algorithm to estimate elasticity and viscosity of tissue. Limitation: The model is applied with a limited range of parameter values in the input scenario.
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