Computerized characterization of contrast enhancement patterns for classifying pulmonary nodules

This paper presents a computerized approach to characterize pulmonary nodules as benign or malignant based on contrast enhancement patterns extracted from serial three-dimensional (3-D) thoracic CT images. In this approach the registration procedure of sequential 3-D pulmonary images consisted of the rigid transformation between two sequential region-of-interest (ROI) images including the pulmonary nodule. The normalized mutual information was used as a voxel-based similarity measure in the registration. After motion correction between successive ROI images, the enhancement rate within a core of the segmented 3-D nodule image was estimated from the difference between the preand post-contrast images. We analyzed a data set of twelve 3-D thoracic CT images with pulmonary nodules in this study. Based on the Wilcoxon rank sum test, the median enhancement of the malignant lesions was significantly higher than that of the benign lesions (p<0.01). The preliminary results of the approach are very promising in characterizing pulmonary nodules based on quantitative measures of the contrast enhancement.