  Arabidopsis thaliana is a plant species widely utilized by scientists to
estimate the impact of genetic differences in root morphological features. For
this purpose, images of this plant after genetic modifications are taken to
study differences in the root architecture. This task requires manual
segmentations of radicular structures, although this is a particularly tedious
and time-consuming labor. In this work, we present an unsupervised method for
Arabidopsis thaliana root segmentation based on morphological operations and
fully-connected Conditional Random Fields. Although other approaches have been
proposed to this purpose, all of them are based on more complex and expensive
imaging modalities. Our results prove that our method can be easily applied
over images taken using conventional scanners, with a minor user intervention.
A first data set, our results and a fully open source implementation are
available online.
