Patch similarity in ultrasound images with hypothesis testing and stochastic distances.

Patch-based techniques have been largely applied to process ultrasound (US) images, with applications in various fields as denoising, segmentation, and registration. An important aspect of the performance of these techniques is how to measure the similarity between patches. While it is usual to base the similarity on the Euclidean distance when processing images corrupted by additive Gaussian noise, finding measures suitable for the multiplicative nature of the speckle in US images is still an open research. In this work, we propose new stochastic distances based on the statistical characteristics of speckle in US. Additionally, we derive statistical measures to compose hypothesis tests that allow a quantitative decision on the patch similarity of US images. Good results with experiments in denoising, segmentation and selecting similar patches confirm the potential of the proposed measures.

Geodesic Distances in Probabilistic Spaces for Patch-Based Ultrasound Image Processing.

Many recent ultrasound image processing methods are based on patch comparison, such as filtering and segmentation. Identifying similar patches in noise-corrupted images is a key factor for the performance of these methods. While the Euclidean distance is ideal to handle the patch comparison under additive Gaussian noise, finding good measures to compare patches corrupted by multiplicative noise is still an open research. In this paper, we deduce several new geodesic distances, arising from parametric probabilistic spaces, and suggest them as similarity measures to process RF and log-compressed ultrasound images in patch-based methods. We provide practical examples using these measures in the fields of ultrasound image filtering and segmentation, with results that confirm the potential of the technique.

Ultrasound Image Despeckling Using Stochastic Distance-Based BM3D.

Ultrasound image despeckling is an important research field, since it can improve the interpretability of one of the main categories of medical imaging. Many techniques have been tried over the years for ultrasound despeckling, and more recently, a great deal of attention has been focused on patch-based methods, such as non-local means and block-matching collaborative filtering (BM3D). A common idea in these recent methods is the measure of distance between patches, originally proposed as the Euclidean distance, for filtering additive white Gaussian noise. In this paper, we derive new stochastic distances for the Fisher-Tippett distribution, based on well-known statistical divergences, and use them as patch distance measures in a modified version of the BM3D algorithm for despeckling log-compressed ultrasound images. State-of-the-art results in filtering simulated, synthetic, and real ultrasound images confirm the potential of the proposed approach.