  The first step to study lower bounds for a stochastic process is to prove a
special property - Sudakov minoration. The property means that if a certain
number of points from the index set are well separated then we can provide an
optimal type lower bound for the mean value of the supremum of the process.
Together with the generic chaining argument the property can be used to fully
characterize the mean value of the supremum of the stochastic process. In this
article we prove the property for canonical processes based on radial-type log
concave measures.
