  This work describes a novel methodology for automatic contour extraction from
2D images of 3D neurons (e.g. camera lucida images and other types of 2D
microscopy). Most contour-based shape analysis methods can not be used to
characterize such cells because of overlaps between neuronal processes. The
proposed framework is specifically aimed at the problem of contour following
even in presence of multiple overlaps. First, the input image is preprocessed
in order to obtain an 8-connected skeleton with one-pixel-wide branches, as
well as a set of critical regions (i.e., bifurcations and crossings). Next, for
each subtree, the tracking stage iteratively labels all valid pixel of
branches, up to a critical region, where it determines the suitable direction
to proceed. Finally, the labeled skeleton segments are followed in order to
yield the parametric contour of the neuronal shape under analysis. The reported
system was successfully tested with respect to several images and the results
from a set of three neuron images are presented here, each pertaining to a
different class, i.e. alpha, delta and epsilon ganglion cells, containing a
total of 34 crossings. The algorithms successfully got across all these
overlaps. The method has also been found to exhibit robustness even for images
with close parallel segments. The proposed method is robust and may be
implemented in an efficient manner. The introduction of this approach should
pave the way for more systematic application of contour-based shape analysis
methods in neuronal morphology.
