A new ultrasound despeckling method through adaptive threshold

TL;DR

This paper introduces a novel ultrasound despeckling technique utilizing a quantum-inspired adaptive threshold to effectively reduce noise while preserving image details.

Contribution

It presents a new despeckling method combining spectrum equalization and quantum-inspired wavelet thresholding, improving noise reduction in ultrasound images.

Findings

Effective noise reduction in real and simulated ultrasound data

Preserves image details and textures better than existing methods

Demonstrates competitive performance in speckle noise removal

Abstract

An efficient despeckling method using a quantum-inspired adaptive threshold function is presented for reducing noise of ultrasound images. In the first step, the ultrasound image is decorrelated by an spectrum equalization procedure due to the fact that speckle noise is neither Gaussian nor white. In fact, a linear filter is exploited to flatten the power spectral density (PSD) of the ultrasound image. Then, the proposed method shrinks complex wavelet coefficients based on the quantum-inspired adaptive threshold function. The proposed approach has been used to denoise both real and simulated data sets and compare with other widely adopted techniques. Experimental results demonstrate that the proposed method has a competitive performance to remove speckle noise and can preserve details and textures of medical ultrasound images.