Speckle noise reduction in medical ultrasound image using monogenic wavelet and Laplace mixture distribution

Medical ultrasound images are corrupted with speckle noise inherently, which can cause negative effects on image-based interpretation and diagnostic procedure. Speckle reduction is an important step prior to the processing and analysis of the medical ultrasound images. In this study, a new speckle noise reduction algorithm in medical ultrasound images is proposed by employing monogenic wavelet transform (MWT) and Bayesian framework. The monogenic coefficients are modeled as the sum of noise-free component plus speckle noise component. First, the MWT coefficients of noise free signal and speckle noise signal are modeled as Laplace mixture distribution and Rayleigh distribution, respectively. Then, the new Bayesian minimum mean square error estimator is derived for the speckle noise reduction. Finally, we estimate the parameters of the proposed de-speckling algorithm by using the expectation maximization algorithm. To evaluate the effectiveness of the proposed de-speckling algorithm, we use both real medical ultrasound images and synthetic images for speckle reduction. The experimental results demonstrate that the proposed algorithm outperforms other state-of-the-art medical ultrasound image de-speckling algorithms by using quantitative indices.