In coronary calcium scoring, motion artifacts affecting calcified plaques are commonly characterized using descriptive terms, which incorporate an element of subjectivity in their interpretations. Quantitative indices may improve the objective characterization of these motion artifacts. In this paper, an automated method for generating 12 quantitative indices, i.e., features that characterize the motion artifacts affecting calcified plaques, is presented. This method consists of using the rapid phase-correlated region-of-interest (ROI) tracking algorithm for reconstructing ROI images of calcified plaques automatically from the projection data obtained during a cardiac scan, and applying methods for extracting features from these images. The 12 features include two dynamic, six morphological, and four intensity-based features. The two dynamic features are three-dimensional (3D) velocity and 3D acceleration. The six morphological features include edge-based volume, threshold-based volume, sphericity, irregularity, average margin gradient, and variance of margin gradient. The four intensity-based features are maximum intensity, mean intensity, minimum intensity, and standard deviation of intensity. The 12 features were extracted from 54 reconstructed sets of simulated four-dimensional images from the dynamic NCAT phantom involving six calcified plaques under nine heart rate/multi-sector gating combinations. In order to determine how well the 12 features correlated with a plaque motion index, which was derived from the trajectory of the plaque, partial correlation coefficients adjusted for heart rate, number of gated sectors, and mean feature values of the six plaques were calculated for all 12 features. Features exhibiting stronger correlations ([r] epsilon [0.60,1.00]) with the motion index were 3D velocity, maximum intensity, and standard deviation of intensity. Features demonstrating stronger correlations ([r] epsilon [0.60, 1.00]) with other features mostly involved intensity-based features. Edge-based volume/irregularity and average margin gradient/variance of margin gradient were the only two feature pairs out of 12 with stronger correlations that did not involve intensity-based features. Automatically extracted features of the motion artifacts affecting calcified plaques in cardiac computed tomography images potentially can be used to develop models for predicting image assessability with respect to motion artifacts.