Speckle quality

Speckle is an important feature of ultrasound images. It can be the basis for tissue classification, volume segmentation, motion estimation etc... As a consequence, methods which are not able to preserve the speckle quality will be penalized in this challenge.

We have generated phantoms that should lead to images with a fully developped speckle for the background (to this aim, in all the simulations we have put around 20 scatterers per resolution cell). In this condition, it is well-know that the intensity of the envelope image should follow a Rayleigh distribution. For a set of predefined regions, the Kolmogorov–Smirnov (KS) test is then applied. This tool is a widely used statistical hypothesis test that verifies in our case whether there is enough evidence in data to deduce that the hypothesis under consideration (i.e. the data follows a Rayleigh distribution) is true. The tested regions that pass the KS test with significance level α = 0.05 are considered as region where the speckle quality is preserved.

In this way, if one of the tested regions fails to pass the KS test, a penality of 40 points will be affected to the participant.

In the figure bellow we show an example of the evaluation of the speckle quality on an experimental image where the KS test was succesfully passed for each tested region.