We are developing a computerized approach to characterize pulmonary nodules through quantitative analysis between sequential 3-D thoracic images. In this approach the registration procedure of sequential 3-D pulmonary images consisted of two transformation steps: the rigid transformation step between two sequential 3-D thoracic CT images and the affine transformation step between two sequential region-of-interest (ROI) images including the pulmonary nodule. In both transformation step, the normalized mutual information was used as a voxel-based similarity measure. After the registration procedure, the 3-D pulmonary nodule image was segmented from the ROI image by a deformable surface method. The curvatures of each voxel in the nodule were computed directly from the gray-level 3-D image. Through curvatures a local description of the lesion was obtained by using shape index, curvedness, and CT value. Based on this local description of the nodule, the evolution of geometrical parameters was tracked through the time interval. Additionally, to characterize globally the evolution of the local description, the shape and the curvedness spectra were introduced. The interval changes of the lesion were traced in the feature spaces. The application results of our method to the sequence of 3-D thoracic images demonstrated that the interval changes of pulmonary nodules could be made visible.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.