  Functional Magnetic Resonance Images acquired during resting-state provide
information about the functional organization of the brain through measuring
correlations between brain areas. Independent components analysis is the
reference approach to estimate spatial components from weakly structured data
such as brain signal time courses; each of these components may be referred to
as a brain network and the whole set of components can be conceptualized as a
brain functional atlas. Recently, new methods using a sparsity prior have
emerged to deal with low signal-to-noise ratio data. However, even when using
sophisticated priors, the results may not be very sparse and most often do not
separate the spatial components into brain regions. This work presents
post-processing techniques that automatically sparsify brain maps and separate
regions properly using geometric operations, and compares these techniques
according to faithfulness to data and stability metrics. In particular, among
threshold-based approaches, hysteresis thresholding and random walker
segmentation, the latter improves significantly the stability of both dense and
sparse models.
