  The problem of non-rigid point set registration is a key problem for many
computer vision tasks. In many cases the nature of the data or capabilities of
the point detection algorithms can give us some prior information on point sets
distribution. In non-rigid case this information is able to drastically improve
registration results by limiting number of possible solutions. In this paper we
explore use of prior information about point sets clustering, such information
can be obtained with preliminary segmentation. We extend existing probabilistic
framework for fitting two level Gaussian mixture model and derive closed form
solution for maximization step of the EM algorithm. This enables us to improve
method accuracy with almost no performance loss. We evaluate our approach and
compare the Cluster Coherent Point Drift with other existing non-rigid point
set registration methods and show it's advantages for digital medicine tasks,
especially for heart template model personalization using patient's medical
data.
