Navigation and interpretation of ultrasound (US) images require substantial expertise, the training of which can
be aided by virtual-reality simulators. However, a major challenge in creating plausible simulated US images
is the generation of realistic ultrasound speckle. Since typical ultrasound speckle exhibits many properties of
Markov Random Fields, it is conceivable to use texture synthesis for generating plausible US appearance. In
this work, we investigate popular classes of texture synthesis methods for generating realistic US content. In
a user study, we evaluate their performance for reproducing homogeneous tissue regions in B-mode US images
from small image samples of similar tissue and report the best-performing synthesis methods. We further show
that regression trees can be used on speckle texture features to learn a predictor for US realism.
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