Ultrasound imaging is one of the most important diagnostic techniques of modern medical imaging. However, due to the presence of multiplicative speckle noise, the development of ultrasound imaging has been limited. For this problem, an improved strategy for Bayesian Non-Local Means (NLM) filtering algorithm was proposed. Firstly,a Bayesian formulation was applied to derive an NLM filter adapted to a relevant ultrasound noise model, which leads to two methods of calculating distance between the image blocks, the Pearson distance and the root distance. Secondly, to lighten the computational burden, a image block pre-selection process was used to accelerate the algorithm when a similar image block was selected in the non-local area. In addition, the relationship between parameter and noise variance was determined experimentally, which made the parameter being adaptive to the noise.Finally, the VS (Visual Studio) and OpenCV (Open source Computer Visual library) were used to realize the algorithm, making the program running time greatly reduced. In order to evaluate the denoising performance of the proposed algorithm, experiments were conducted on both phantom images and real ultrasound images. The experimental results show that the algorithm has a great improvement in the performance of removing speckle noise and achieves satisfactory results in terms of preserving the edges and image details, compared with some existing classical algorithms.