Mei Kunqiang, Hu Bin, Fei Baowei, Qin Binjie
IEEE Trans Image Process. 2019 Nov 19. doi: 10.1109/TIP.2019.2953361.
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.
我们提出了一种超声斑点滤波方法，该方法不仅能保留各种边缘特征，还能滤除超声图像中与组织相关的复杂斑点噪声。关键思想是使用一种基于相位一致性的边缘显著性度量——相位不对称性（PAS）来检测这些不同的边缘，PAS对边缘的强度幅度不变，在非边缘平滑区域取值为0，在理想的阶跃边缘取值为1，在缓慢变化的斜坡边缘也取中间值。通过在设计加权系数时利用PAS度量，以在分数阶各向异性扩散和全变分（TV）滤波器的TV代价函数之间保持平衡，我们提出了一种新的分数阶TV框架，该框架不仅能在保留斜坡边缘的情况下实现最佳的去斑性能，还能减少整数阶滤波器产生的阶梯效应。然后，我们在设计新的分数阶扩散系数时利用PAS度量，以在扩散滤波中适当保留低对比度边缘。最后，与固定分数阶扩散滤波器不同，基于PAS度量引入了自适应分数阶，以增强物体之间空间过渡区域的各种弱边缘。使用梯度下降法对提出的分数阶TV模型进行最小化，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，该方法在斑点减少和特征保留方面均优于其他现有超声去斑滤波器。在三种不同噪声水平下，特征相似性指标的最佳得分分别达到了0.867、0.844和0.834，而在平均和中位数骰子相似系数方面，最佳乳腺超声分割准确率分别为96.25%和96.15%。
