This paper introduces two new methods for the automatic anatomical and functional analysis of neurobiological autoradiographic image stacks, such as 2-fluoro-deoxyglucose (2FDG) images. The difficulty in the evaluation of these "2(1/2)D" datasets is that they do not inherently represent a continuous 3D data volume (as generated by MRI or CT), but consist of a stack of images from single tissue slices, suffering from unavoidable preparation artifacts. In the first part of the paper, a semi-automatic segmentation method is presented which generates a 3D surface model of certain brain structures and which is robust against different cutting directions with respect to the brain coordinate system. The method saves man-hours compared to manual segmentation and the results are highly reproducible. In the second part, a fully automatic method for the extraction, analysis and 3D visualization of functional information is described, which allows not only a more accurate localization of activation sites, but also greatly enhances the comparability of different individuals. Results are shown for 2FDG autoradiographs from rat brains under acoustical stimulation.