Automated or semi-automated analysis and labeling of structural brain images, such as magnetic resonance (MR) and computed tomography, is desirable for a number of reasons. Quantification of brain volumes can aid in the study of various diseases and the affect of various drug regimes. A labeled structural image, when registered with a functional image such as positron emission tomography or single photon emission computed tomography, allows the quantification of activity in various brain subvolumes such as the major lobes. Because even low resolution scans (7.5 to 8.0 mm slices) have 15 to 17 slices in order to image the entire head of the subject hand segmentation of these slices is a very laborious process. However, because of the spatial complexity of many of the brain structures notably the ventricles, automatic segmentation is not a simple undertaking. In order to accurately segment a structure such as the ventricles we must have a model of equal complexity to guide the segmentation. Also, we must have a model which can incorporate the variability among different subjects from a pre-specified group. Analysis of MR brain scans is accomplished by utilizing the data from T2 weighted and proton density images to isolate the regions of interest. Identification is then done automatically with the aid of a composite model formed from the operator assisted segmentation of MR scans of subjects from the same group. We describe the construction of the model and demonstrate its use in the segmentation and labeling of the ventricles in the brain.