Speckle noise is a granular structure

and it occurs when a coherent source and a non-coherent detector are used to interrogate a medium. Speckle noise is an undesirable part in the ultrasound image

since it can mask the small difference in grey level and degrade the image quality. The task of despeckling is an important step for analysis and processing of ultrasound images

which is essential for automatic diagnostic techniques. The wide spread of mobile and portable ultrasound scanning instruments also necessitates that a clearer image must be obtained to the medical practitioner. A novel despeckling algorithm for medical ultrasound images is proposed

which is based on the wavelet transformation and a bilateral filter. According to the statistical properties of medical ultrasound images in wavelet domain

an improved wavelet threshold function is proposed on the basis of the universal wavelet threshold function. The proposed wavelet threshold function is obtained by multiplying the universal wavelet threshold function with an adjustable parameter. The noise-free signal and speckle noise in the wavelet domain are modeled as generalized Laplace distribution and Gaussian distribution respectively. The Bayesian maximum a posteriori estimation is applied to get a new wavelet shrinkage algorithm. The speckle noise in the high-pass component in wavelet domain of ultrasound images is suppressed by the new wavelet shrinkage algorithm. The speckle noise in the low-pass approximation component is filtered by the bilateral filter

since the low-pass approximation component of ultrasound images also contains some speckle noise. The filtered image is then obtained via inverse wavelet transform. The comparative experiments with seven other despeckling methods are conducted. Several image quality metrics are used to compare the performance of speckle reduction

such as the peak signal to noise ratio (PSNR)

the structure similarity (SSIM) and Pratt's figure of merit (FoM)

as well as the computational time of different methods is presented. The filtered images of the proposed algorithm get the best result by compared to the PSNR and FoM values with other seven despeckling methods. The best result of the PSNR and FoM value means that the proposed algorithm can suppress more speckle noise and the filtered image has the similar edge to witch of the noise-reference synthetic image. In the comparison of SSIM values

the proposed algorithm also gets good performance

which means that the proposed algorithm can retain a structure similar structure to the noise-free reference synthetic ultrasound image. Observing the computational time

the proposed algorithm does not have superiority in the aspect of time consuming. The experiment of clinical ultrasound breast images with lesions is also conducted

and we can find that the proposed algorithm gets a pretty good despeckling performance. Since speckle noise limits the development of automatic diagnostic technology for ultrasound images

we propose an improved despeckling algorithm on the basis of the wavelet transform and the bilateral filter. The experiments of synthetic ultrasound images and clinical ultrasound breast images show that the proposed despeckling algorithm not only has better speckle reduction than the other seven filters

but also can preserve image details such as the edge of lesions.