Electron tomography allows three-dimensional visualization of cellular landscapes in molecular detail. Segmentation is a paramount stage for the interpretation of the reconstructed tomograms. Although several computational approaches have been proposed, none has prevailed as a generic method and thus segmentation through manual annotation is still a common choice. In this work we introduce a segmentation method targeted at membranes, which define the natural limits of compartments within biological specimens. Our method is based on local differential structure and on a Gaussian-like membrane model. First, it isolates information through scale-space and finds potential membrane-like points at a local scale. Then, the structural information is integrated at a global scale to yield the definite segmentation. We show and validate the performance of the algorithm on a number of tomograms under different experimental conditions. (C) 2011 Elsevier Inc. All rights reserved.