A new ultrasound despeckling method through adaptive threshold
This work addresses noise reduction in medical ultrasound imaging, which is incremental as it builds on existing despeckling methods with a novel threshold approach.
The authors tackled the problem of speckle noise in ultrasound images by proposing a quantum-inspired adaptive threshold function for despeckling, achieving competitive performance in preserving details and textures compared to other techniques.
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.