Bayesian image segmentation of transmission electron tomography 3D reconstructions

The use of transmission electron tomography for three dimensional morphology analysis of nanostructured materials has grown significantly in recent years due to the improved robustness of automated image collection, alignment and reconstruction routines. Although the amount of published 3D electron tomography data has increased accordingly, in many cases the data is used purely for visualization purposes, due to the difficulty of making quantitative measurements from the as-reconstructed data. This is due to the traditionally difficult step of segmentation of the 3D image volume, which has been hindered due to noise, artifacts and lack of necessary image detail or sharpness. In this work the Simultaneous Iterative Reconstruction Technique (SIRT) is used to reconstruct High Angle Annular Dark Field-Scanning Transmission Electron Microscopy (HAADF-STEM) tomography data. The number of SIRT iterations is varied to optimize the reconstruction for segmentation. In particular, signal to noise ratio, signal to artifact ratio, and image blur are identified as parameters which each contribute to the suitability of the reconstruction for segmentation. The Expectation Maximization/ Maximization of Posterior Marginals image segmentation algorithm is applied to the SIRT reconstructions, and the resulting Average Intensity Projection (AIP) of the segmented image stack is compared to the raw tomography projection data.