Objective assessment of pulmonary disease from computed tomography (CT) examinations is desirable but difficult. When such assessments can be made, it is important that they are related to some part of the pathophysiologic process present. Herein we propose that automated volume histogram analysis can yield data that allow differentiation of normal from abnormal lung, and that the magnitude of disease will have an association with objective CT indices. Data from pulmonary CT images from 34 foxes (six uninfected controls and 28 infected with Angiostrongylus vasorum, subdivided by age and infective dose) were available. Lung tissue was segmented from surrounding tissue using an automated segmentation method. A volume histogram showing voxel frequency for each CT number in the range −1024 to −250 HU was created from the entire image stack from each fox. Using these data, the inter-quartile range and the CT number at the 95th percentile were determined. The results showed that segmentation could be readily achieved but that areas of severely diseased lung were excluded. Based on two-way analysis of variance for both the inter-quartile range and the CT number at the 95th percentile, both quantities were significantly affected by the infection status of the animal and were related to worm burden (P<0.001). The study shows that this form of analysis is readily achieved and provides quantitative data that can be used to assess disease severity, progression, and response to treatment.