Abstract
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.
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