On Real-Time Denoising and Compression of Ultrasound Imaging

Background: Reducing speckle noise in ultrasound imaging is an essential pre-processing step for most Computer Aided Diagnostics (CAD) algorithms, as well as for efficient storage and transmission of ultrasound data for telemedicine purposes. However, state-of-the-art methods still suffer from high computational complexity and therefore are not suitable for real-time applications.

Methods: Transform domain filtering (TDF) is one of the simplest denoising techniques yet very effective for natural images. Nevertheless, finding an optimal threshold for the filtering operation is a challenging task, especially in non-Gaussian noise regimes such as speckle noise, which is crucial for satisfying denoising. In this work, we use a patch-based TDF and exploit the fact that different patches do not necessarily have the same diagnostic significance and hence can be filtered with adaptive thresholds. Accordingly, we minimize a constrained sparsity-prior for the transform coefficients, thus converting the algorithm to be controlled by a single parameter, called error-threshold, which is more robust and easier to adjust. Based on our statistical analysis of the speckle noise, we develop a method for estimating the optimal error-threshold by means of Peak-Signal-to-Noise-Ratio (PSNR) with respect to a de-speckled image that is obtained by established de-speckling algorithms, such as Non-Local Means (NLM).

Results: Based on experiments performed over in-vivo ultrasound images, we show that the proposed method significantly reduces the computational complexity, while achieving a performance level that is visually indistinguishable with respect to the pre-determined de-speckling method.

Conclusion: The proposed method can efficiently denoise and compress ultrasound imaging for real-time purposes.