Enhanced Wiener filter for ultrasound image restoration

Background and objective: Speckle phenomenon strongly affects UltraSound (US) images. In the last years, several efforts have been done in order to provide an effective denoising methodology. Although good results have been achieved in terms of noise reduction effectiveness, most of the proposed approaches are not characterized by low computational burden and require the supervision of an external operator for tuning the input parameters.

Methods: Within this manuscript, a novel approach is investigated, based on Wiener filter. Working in the frequency domain, it is characterized by high computational efficiency. With respect to classical Wiener filter, the proposed Enhanced Wiener filter is able to locally adapt itself by tuning its kernel in order to combine edges and details preservation with effective noise reduction. This characteristic is achieved by implementing a Local Gaussian Markov Random Field for modeling the image. Due to its intrinsic characteristics, the computational burden of the algorithm is sensibly low compared to other widely adopted filters and the parameter tuning effort is minimal, being well suited for quasi real time applications.

Results: The approach has been tested on both simulated and real datasets, showing interesting performances compared to other state of art methods.

Conclusions: A novel denoising method for UltraSound images is proposed. The approach is able to combine low computational burden with interesting denoising performances and details preservation.