We have previously reported the creation of software to perform elastic matching of medical images, for example, to compare an idealized atlas with a set of computer tomography (CT) images. In order to evaluate the performance of this software, we have created a digitized atlas from a young normal brain, using 135 myelin-stained sections at 700 micron spacing. Software was written in C on a Hewlett-Packard workstation to allow the entering and editing of regional anatomical contours. The 2-D contours are stacked to create a 3-D atlas that can be rotated, scaled, and resliced in the three standard imaging planes. For each patient being analyzed, an individualized atlas is created from this idealized atlas by elastically matching the atlas to the patient's CT scans. Matching is achieved in two steps - global registration first, followed by deformation of the atlas to match the contours of the CT brain. This is done iteratively at coarse, medium and high resolution to achieve the best results. During this process, all regional contours are also dis-placed and deformed. We have evaluated how well the computer places these regional contours by having four experts outline several subcortical structures on the CT scans of six patients. Their degree of overlap and the variability in positioning were measured and compared with the placement of the same regions of interest by the computer. In one third of the structures there was no difference between the computer and the experts. In 18% of the regions the computer-defined region was closer to the truth than at least one of the experts. When the computer differed, it was usually by 2-3 mm in both x and y, and frequently the computer-defined region was inscribed within the expert's. This is a preliminary test of the system, using only one set of elastic coefficients, one processing variant, and only subcortical structures. The results are promising and techniques are being implemented to overcome any current deficiencies. A more systematic evaluation of the entire process, exploring all the variables that affect the accuracy of elastic matching and adding cortical regions of interest, will follow in the near future. The elastic matching produces an individualized template of regions that can be super-imposed on the anatomical images. The user may then interactively modify these regions based on the observed anatomy, delete or add regions, and extract data from these user-adjusted overlays. The flexibility inherent in this approach allows data analysis with both standard and non-standard approaches, with regions defined both by the anatomy and by areas of functional activity.