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. The best scores on feature similarity indices have achieved 0.867, 0.844 and 0.834 under three different levels of noise, while the best breast ultrasound segmentation accuracy in terms of the mean and median dice similarity coefficient are 96.25% and 96.15%, respectively.

中文翻译：

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具有分数各向异性扩散和总变化的相位不对称超声去斑。


我们提出了一种超声散斑过滤方法，不仅可以保留各种边缘特征，还可以过滤超声图像中与组织相关的复杂散斑噪声。关键思想是使用基于相位同余的边缘显着性度量（称为相位不对称性 (PAS)）来检测这些不同的边缘，该度量对于边缘的强度幅度是不变的，并且在非边缘平滑区域中取 0，在想法步长边缘处取 1 ，同时也在缓慢变化的斜坡边缘处取中间值。通过在设计加权系数时利用 PAS 度量来维持 TV 成本函数中分数阶各向异性扩散和全变分 (TV) 滤波器之间的平衡，我们提出了一种新的分数 TV 框架，不仅可以实现具有斜坡边缘保留的最佳去斑性能而且还减少了积分阶滤波器产生的阶梯效应。然后，我们利用 PAS 度量设计新的分数阶扩散系数，以在扩散滤波中正确保留低对比度边缘。最后，与固定分数阶扩散滤波器不同，基于PAS度量引入自适应分数阶，以增强对象之间空间过渡区域中的各种弱边缘。使用梯度下降法最小化所提出的分数电视模型以获得最终的去噪图像。超声乳腺图像分割的实验结果和实际应用表明，在视觉评估和定量指标方面，该方法在散斑减少和特征保留方面均优于其他最先进的超声去斑滤波器。特征相似度指数的最佳得分为0.867、0.844和0。834在三种不同水平的噪声下，而最佳乳腺超声分割精度在平均和中值骰子相似系数方面分别为96.25%和96.15%。