A new speckle filtering method for ultrasound images based on a weighted multiplicative total variation

• We propose a new approach for speckle image de-noising while preserving important features.
• The de-noising process is performed using a multiplicative regularization method.
• Proposed method combines a data misfit and a Weighted Total Variation function as a multiplicative factor in the cost functional.
• Smoothing process is achieved on an adaptive window whose shapes, sizes and orientations vary with the image.
• Results on synthetic and real images have demonstrated efficiency and robustness of the proposed method compared to well-established methods.

Ultrasound images are corrupted by a multiplicative noise – the speckle – which makes hard high level image analysis. In order to solve the difficulty of designing a filter for an effective speckle removing, we propose a new approach for de-noising images while preserving important features. This method combines a data misfit function based on Loupas et al. model and a Weighted Total Variation (WTV) function as a multiplicative factor in the cost functional. The de-noising process is performed using a multiplicative regularization method through an adaptive window whose shapes, sizes and orientations vary with the image structure. Instead of performing the smoothing uniformly, the process is achieved in preferred orientations, more in homogeneous areas than in detailed ones to preserve region boundaries while reducing speckle noise within regions. Quantitative results on synthetic and real images have demonstrated the efficiency and the robustness of the proposed method compared to well-established and state-of-the-art methods. The speckle is removed while edges and structural details of the image are preserved.