“Electron tomography based on a total variation minimization reconstruction technique”. Goris B, van den Broek W, Batenburg KJ, Heidari Mezerji H, Bals S, Ultramicroscopy 113, 120 (2012). http://doi.org/10.1016/j.ultramic.2011.11.004
Abstract: The 3D reconstruction of a tilt series for electron tomography is mostly carried out using the weighted backprojection (WBP) algorithm or using one of the iterative algorithms such as the simultaneous iterative reconstruction technique (SIRT). However, it is known that these reconstruction algorithms cannot compensate for the missing wedge. Here, we apply a new reconstruction algorithm for electron tomography, which is based on compressive sensing. This is a field in image processing specialized in finding a sparse solution or a solution with a sparse gradient to a set of ill-posed linear equations. Therefore, it can be applied to electron tomography where the reconstructed objects often have a sparse gradient at the nanoscale. Using a combination of different simulated and experimental datasets, it is shown that missing wedge artefacts are reduced in the final reconstruction. Moreover, it seems that the reconstructed datasets have a higher fidelity and are easier to segment in comparison to reconstructions obtained by more conventional iterative algorithms.
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT); Vision lab
Impact Factor: 2.843
Times cited: 171
DOI: 10.1016/j.ultramic.2011.11.004
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“Ultra-high resolution electron tomography for materials science : a roadmap”. Batenburg KJ, Bals S, Van Aert S, Roelandts T, Sijbers J, Microscopy and microanalysis 17, 934 (2011). http://doi.org/10.1017/S143192761100554X
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT); Vision lab
Impact Factor: 1.891
DOI: 10.1017/S143192761100554X
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“Accurate segmentation of dense nanoparticles by partially discrete electron tomography”. Roelandts T, Batenburg KJ, Biermans E, Kübel C, Bals S, Sijbers J, Ultramicroscopy 114, 96 (2012). http://doi.org/10.1016/j.ultramic.2011.12.003
Abstract: Accurate segmentation of nanoparticles within various matrix materials is a difficult problem in electron tomography. Due to artifacts related to image series acquisition and reconstruction, global thresholding of reconstructions computed by established algorithms, such as weighted backprojection or SIRT, may result in unreliable and subjective segmentations. In this paper, we introduce the Partially Discrete Algebraic Reconstruction Technique (PDART) for computing accurate segmentations of dense nanoparticles of constant composition. The particles are segmented directly by the reconstruction algorithm, while the surrounding regions are reconstructed using continuously varying gray levels. As no properties are assumed for the other compositions of the sample, the technique can be applied to any sample where dense nanoparticles must be segmented, regardless of the surrounding compositions. For both experimental and simulated data, it is shown that PDART yields significantly more accurate segmentations than those obtained by optimal global thresholding of the SIRT reconstruction.
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT); Vision lab
Impact Factor: 2.843
Times cited: 34
DOI: 10.1016/j.ultramic.2011.12.003
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