Polyp size is an important feature descriptor for clinical classification and follow-up decision making in CT colonography. Currently, polyp size is measured from computed tomography (CT) studies manually as the single largest dimension of the polyp head, excluding the stalk if present, in either multi-planar reconstruction (MPR) or three-dimensional (3D) views. Manual measurements are subject to intra- and inter-reader variation, and can be time-consuming. Automated polyp segmentation and size measurement can reduce the variability and speed up the process. In this study, an automated polyp size measurement technique is developed. Using this technique, the polyp is segmented from the attached healthy tissue using a novel, model-based approach. The largest diameter of the segmented polyp is measured in axial, sagitttal and coronal MPR views. An expert radiologist identified 48 polyps from either supine or prone views of 52 cases of the Walter-Reed virtual colonoscopy database. Automated polyp size measurements were carried out and compared with the manual ones. For comparison, three different statistical methods were used: overall agreement using chance-corrected kappa indices; the mean absolute differences; and Bland-Altman limits of agreement. Manual and automated measurements show good agreement both in 2D and 3D views.