The mean linear intercept (MLI) score is a useful and common approach for quantifying lung structure in histopathological images. This thesis describes a system developed to calculate the MLI score in a fully automated manner. The system was tested using 20 WSIs from mice. The root-mean-squared deviation between the MLI score of the proposed method and a human rater was 5.73 (standard deviation 5.65), and there was a very strong correlation (r=0.9931). Biases for the indirect method of MLI scoring are examined and shown to account for the differences with the direct MLI scores. Results suggest that shorter guideline length and smaller number of accepted FOV images have a higher standard error when estimating the MLI score when compared to longer guideline lengths and higher number of accepted FOV images. The proposed automated system provides an efficient, accurate, and accessible method that could replace current manual and semi-automated techniques.