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Advances in automated data acquisition in electron tomography have led to an explosion in the amount of data that can be obtained about the spatial architecture of a variety of biologically and medically relevant objects with resolutions in the "nano" range of 10-1000 nm. The development of methods to automatically analyze the vast amounts of information contained in these tomograms is a major challenge since the electron tomograms are intrinsically very noisy. A fundamental step in the automatic analysis of large amounts of data for statistical inference is to segment relevant 3D features in cellular tomograms. Procedures for segmentation must work robustly and rapidly in spite of the low signal to noise ratios inherent to biological electron microscopy. This work first evaluates various non-linear denoising techniques on tomograms recorded at cryogenic temperatures. Using datasets of bacterial tomograms as an example, we demonstrate that non-linear diffusion techniques significantly improve the fidelity of automated feature extraction. Our approach represents an important step in automating the efficient extraction of useful information from large datasets in biological tomography, and facilitates the overall goal of speeding up the process of reducing gigabyte-sized tomograms to relevant byte-sized data.