Data from: Robust extraction of quantitative structural information from high-variance histological images of livers from necropsied Soay sheep

Quantitative information is essential to the empirical analysis of
biological systems. In many such systems, spatial relations between
anatomical structures is of interest, making imaging a valuable data
acquisition tool. However, image data can be difficult to analyse
quantitatively. Many image processing algorithms are highly sensitive to
variations in the image, limiting their current application to fields
where sample and image quality may be very high. Here, we develop robust
image processing algorithms for extracting structural information from a
dataset of high-variance histological images of inflamed liver tissue
obtained during necropsies of wild Soay sheep. We demonstrate that
features of the data can be measured in a fully automated manner,
providing quantitative information which can be readily used in
statistical analysis. We show that these methods provide measures that
correlate well with a manual, expert operator-led analysis of the same
images, that they provide advantages in terms of sampling a wider range of
information and that information can be extracted far more quickly than in
manual analysis.