Adaptive fractional-order differentiation filter guided by feature asymmetry for feature-preserving ultrasound despeckling

In ultrasound despeckling, it is essential to remove speckle noise with satisfactory feature preservation for better diagnosis and analysis in many applications. This paper proposes an adaptive fractional-order differentiation filter guided by feature asymmetry for feature-preserving ultrasound despeckling. Since fractional anisotropic diffusion (FAD) filter performs well in smooth regions while fractional total variation (FTV) filter works better near features, our framework combines the FAD filter and FTV filter to maintain their advantages. Moreover, the feature significance calculated by using feature asymmetry is integrated into the diffusion coefficient of the FAD filter to protect low contrast features. Finally, rather than adopting one fixed fractional order, the proposed filter adaptively assigns fractional order on the basis of the feature significance to further preserve features. Experiments on synthetic and clinic ultrasound images demonstrate that the proposed filter performs better in both speckle reduction and feature preservation compared with other state-of-the-art ultrasound speckle reduction filters.