Most Computer-Aided Diagnosis (CAD) research studies are performed using a single type of Computer
Tomography (CT) scanner and therefore, do not take into account the effect of differences in the imaging acquisition
scanner parameters. In this paper, we present a study on the effect of the CT parameters on the low-level image features
automatically extracted from CT images for lung nodule interpretation. The study is an extension of our previous study
where we showed that image features can be used to predict semantic characteristics of lung nodules such as margin,
lobulation, spiculation, and texture. Using the Lung Image Data Consortium (LIDC) dataset, we propose to integrate the
imaging acquisition parameters with the low-level image features to generate classification models for the nodules'
semantic characteristics. Our preliminary results identify seven CT parameters (convolution kernel, reconstruction
diameter, exposure, nodule location along the z-axis, distance source to patient, slice thickness, and kVp) as influential in
producing classification rules for the LIDC semantic characteristics. Further post-processing analysis, which included
running box plots and binning of values, identified four CT parameters: distance source to patient, kVp, nodule location,
and rescale intercept. The identification of these parameters will create the premises to normalize the image features
across different scanners and, in the long run, generate automatic rules for lung nodules interpretation independently of
the CT scanner types.