  Automating the extraction and quantification of features from
three-dimensional (3-D) image stacks is a critical task for advancing computer
vision research. The union of 3-D image acquisition and analysis enables the
quantification of biological resistance of a plant tissue to fungal infection
through the analysis of attributes such as fungal penetration depth, fungal
mass, and branching of the fungal network of connected cells. From an image
processing perspective, these tasks reduce to segmentation of vessel-like
structures and the extraction of features from their skeletonization. In order
to sample multiple infection events for analysis, we have developed an approach
we refer to as macroscopic microscopy. However, macroscopic microscopy produces
high-resolution image stacks that pose challenges to routine approaches and are
difficult for a human to annotate to obtain ground truth data. We present a
synthetic hyphal network generator, a comparison of several vessel segmentation
methods, and a minimum spanning tree method for connecting small gaps resulting
from imperfections in imaging or incomplete skeletonization of hyphal networks.
Qualitative results are shown for real microscopic data. We believe the
comparison of vessel detectors on macroscopic microscopy data, the synthetic
vessel generator, and the gap closing technique are beneficial to the image
processing community.
