An iterative speckle filtering algorithm for ultrasound images based on bayesian nonlocal means filter model

Simultaneous compression and speckle reduction of clinical breast and fetal ultrasound images using rate-fidelity optimized coding

Medical ultrasound images are inherently noised with speckle noise, which may interfere with Computer Aided Diagnostics (CAD) tasks, such as automatic segmentation. A compression and speckle de-noising method is proposed and tested on real clinical breast and fetal ultrasound images. The proposed algorithm is based on the optimization of quantization coefficients when applying Wavelet representation on the image, where the optimization is held such that a pre-defined mathematical fidelity criterion with respect to a desired de-speckled image is obtained. The proposed algorithm yields effective speckle reduction whilst preserving the edges in the images, with a reduced computational burden compared to other existing state-of-the-art methods, such as Optimal Bayesian Non-Local Means (OBNLM). In addition, the images are simultaneously compressed to a target bit-rate. The proposed algorithm is evaluated using both objective mathematical fidelity criteria (such as Structural Similarity and Edge Preserve) as well as subjective radiologists tests. The experimental results demonstrate the ability of the proposed method to achieve de-speckled images with compression ratios of approximately 30:1, whilst obtaining competitive subjective as well as objective fidelity measures with respect to the desired de-speckled images.

Phase asymmetry ultrasound despeckling with fractional anisotropic diffusion and total variation

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.