Speckle noise reduction method combining total variation and wavelet shrinkage for clinical ultrasound imaging

Ultrasound imaging is a widely used and safe medical diagnostic technique; however, the usefulness of ultrasound imaging is degraded by the presence of signal dependant noise known as speckle. In this paper, we propose a new speckle reduction method and coherence enhancement of ultrasound images based on method that combines total variation (TV) method and wavelet shrinkage. In our method, a noisy image is decomposed into subbands of LL, LH, HL, and HH in wavelet domain. LL subband contains the low frequency coefficients along with less noise, which can be easily eliminated using TV-based method. More edges and other detailed information like textures are contained in the other three subbands, and we propose a shrinkage method based on the local variance to extract them from high frequency noise. The proposed method has been compared with Median, Anisotropic diffusion filtering, Geometric, Mean and variance local statistics, Wavelet and Total variation filter using quantitative parameters. It has been found that quality evaluation metrics the proposed method performs better than all other methods while still retaining the structural details and experimental results show that this method retains the edges and textures very well while removing noise.