Application of the local similarity filter for the suppression of multiplicative noise in medical ultrasound images

In this paper we address the problem of the reduction of multiplicative noise in digital images. This kind of image distortion, also known as speckle noise, severely decreases the quality of medical ultrasound images and therefore their effective enhancement and restoration is of vital importance for proper visual inspection and quantitative measurements. The structure of the proposed Pixel-Patch Similarity Filter (PPSF) is a weighted average of pixels in a processing block and the weights are determined calculating the sum of squared differences between the mean of a patch and the intensities of pixels of the local window at the block center. The structure of the proposed design is similar to the bilateral and non-local means filters, however we neglect the topographic distance between pixels, which decreases substantially its computational complexity. The new technique was evaluated on standard gray scale test images contaminated with multiplicative noise modelled using Gaussian and uniform distribution. Its efficiency was also assessed utilizing a set of simulated ultrasonographic images distorted by means of the Field II simulation software and real ultrasound images of a finger joint. The comparison with the state-of-the-art techniques revealed very high efficiency of the proposed filtering framework, especially for strongly degraded images. Visually, the homogeneous areas are smoother, while image edges and small details are better preserved. The experiments have shown that satisfactory results were obtained with patches consisting of only 9 samples belonging to a relatively small processing block of 7x7 pixels, which ensures low computational complexity of the proposed denoising scheme and allows its application in real-time image processing scenarios.