Multiscale hybrid method for speckle reduction of medical ultrasound images

This paper presents a multi-scale hybrid method for speckle reduction in ultrasound (US) images. Speckle is removed by guided filtering, directional filtering, and fractional order filtering on the coarse to fine resolution images of a wavelet pyramid. For the directional filter, the eigen-analysis of each pixel is firstly carried out to obtain its structural features, and then it is classified into edges for filtering. Speckle noise, corresponding to the homogeneous anatomical regions, is then alleviated by the guided filter. Thereby, the algorithm reduces speckle noise while enhancing edge sharpness regardless of the size of the edges. In the synthetic images, the proposed method showed statistically significant improvements in peak signal-to-noise ratio(PSNR), structural similarity(SSIM), feature similarity index(FSIM) index and Mean Squared Error(MSE) compared with other speckle reduction methods, e.g., the squeeze boxes (SBF) filter, optimal Bayesian NLM (OBNLM) filter, speckle reducing anisotropic diffusion filter (SRAD), nonlocal low-rank framework (NLLRF) and multi-scale attention-guided neural network (MSANN). Similarly, our method outperformed the other methods in terms of mainly metrics. All the clinical images that were denoised using the six speckle reduction methods were reviewed by four radiologists for evaluation based on each radiologist’s diagnostic preferences. All the radiologists showed a significant preference for the liver images and arotid artery images obtained using our methods in terms of effectively suppresses speckle noise while preserving the structural details. For the kidney and thyroid images, our method showed similar improvement over other methods. The experimental results show that this method has better performance than other state-of-the-art medical ultrasonic image speckle removal methods.