  Dynamical contrast enhanced (DCE) imaging allows non invasive access to
tissue micro-vascularization. It appears as a promising tool to build imaging
biomark-ers for diagnostic, prognosis or anti-angiogenesis treatment monitoring
of cancer. However, quantitative analysis of DCE image sequences suffers from
low signal to noise ratio (SNR). SNR may be improved by averaging functional
information in a large region of interest when it is functionally homogeneous.
  We propose a novel method for automatic segmentation of DCE image sequences
into functionally homogeneous regions, called DCE-HiSET. Using an observation
model which depends on one parameter a and is justified a posteri-ori,
DCE-HiSET is a hierarchical clustering algorithm. It uses the p-value of a
multiple equivalence test as dissimilarity measure and consists of two steps.
The first exploits the spatial neighborhood structure to reduce complexity and
takes advantage of the regularity of anatomical features, while the second
recovers (spatially) disconnected homogeneous structures at a larger (global)
scale. Given a minimal expected homogeneity discrepancy for the multiple
equivalence test, both steps stop automatically by controlling the Type I
error. This provides an adaptive choice for the number of clusters. Assuming
that the DCE image sequence is functionally piecewise constant with signals on
each piece sufficiently separated, we prove that DCE-HiSET will retrieve the
exact partition with high probability as soon as the number of images in the
sequence is large enough. The minimal expected homogeneity discrepancy appears
as the tuning parameter controlling the size of the segmentation.
  DCE-HiSET has been implemented in C++ for 2D and 3D image sequences with
competitive speed.
  Keywords : DCE imaging, automatic clustering, hierarchical segmentation,
equivalence test
