Phase asymmetry ultrasound despeckling with fractional anisotropic diffusion and total variation

TL;DR

This paper introduces a novel ultrasound despeckling method that combines phase asymmetry edge detection with fractional anisotropic diffusion and total variation filtering, effectively reducing speckle noise while preserving edges and features.

Contribution

It proposes a new fractional TV framework using phase asymmetry for adaptive despeckling and edge preservation in ultrasound images, outperforming existing methods.

Findings

Enhanced speckle reduction and edge preservation demonstrated in experiments.

Outperforms state-of-the-art filters in visual and quantitative evaluations.

Effective in real ultrasound breast image segmentation applications.

Abstract

We propose an ultrasound speckle filtering method for not only preserving various edge features but also filtering tissue-dependent complex speckle noises in ultrasound images. The key idea is to detect these various edges using a phase congruence-based edge significance measure called phase asymmetry (PAS), which is invariant to the intensity amplitude of edges and takes 0 in non-edge smooth regions and 1 at the idea step edge, while also taking intermediate values at slowly varying ramp edges. By leveraging the PAS metric in designing weighting coefficients to maintain a balance between fractional-order anisotropic diffusion and total variation (TV) filters in TV cost function, we propose a new fractional TV framework to not only achieve the best despeckling performance with ramp edge preservation but also reduce the staircase effect produced by integral-order filters. Then, we exploit the PAS metric in designing a new fractional-order diffusion coefficient to properly preserve low-contrast edges in diffusion filtering. Finally, different from fixed fractional-order diffusion filters, an adaptive fractional order is introduced based on the PAS metric to enhance various weak edges in the spatially transitional areas between objects. The proposed fractional TV model is minimized using the gradient descent method to obtain the final denoised image. The experimental results and real application of ultrasound breast image segmentation show that the proposed method outperforms other state-of-the-art ultrasound despeckling filters for both speckle reduction and feature preservation in terms of visual evaluation and quantitative indices.