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

ABSTRACT: 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 whi

SUBMITTER: Caudron Q 

PROVIDER: S-EPMC5541533 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature