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Author |
Goris, B.; van den Broek, W.; Batenburg, K.J.; Heidari Mezerji, H.; Bals, S. |
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Title |
Electron tomography based on a total variation minimization reconstruction technique |
Type |
A1 Journal article |
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Year |
2012 |
Publication |
Ultramicroscopy |
Abbreviated Journal |
Ultramicroscopy |
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Volume |
113 |
Issue |
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Pages |
120-130 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT); Vision lab |
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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. |
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Corporate Author |
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Thesis |
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Publisher |
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Place of Publication |
Amsterdam |
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Wos |
000300554400006 |
Publication Date |
2011-11-14 |
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Series Editor |
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Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0304-3991; |
ISBN |
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Additional Links  |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
2.843 |
Times cited |
171 |
Open Access |
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Notes |
Fwo |
Approved |
Most recent IF: 2.843; 2012 IF: 2.470 |
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Call Number |
UA @ lucian @ c:irua:93637 |
Serial |
987 |
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Author |
Batenburg, K.J.; Bals, S.; Van Aert, S.; Roelandts, T.; Sijbers, J. |
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Title |
Ultra-high resolution electron tomography for materials science : a roadmap |
Type |
A1 Journal article |
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Year |
2011 |
Publication |
Microscopy and microanalysis |
Abbreviated Journal |
Microsc Microanal |
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Volume |
17 |
Issue |
S:2 |
Pages |
934-935 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT); Vision lab |
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Abstract |
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Thesis |
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Publisher |
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Place of Publication |
Cambridge, Mass. |
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Language |
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Wos |
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Publication Date |
2011-10-07 |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
1431-9276;1435-8115; |
ISBN |
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Additional Links |
UA library record |
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Impact Factor |
1.891 |
Times cited |
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Open Access |
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Notes |
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Approved |
Most recent IF: 1.891; 2011 IF: 3.007 |
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Call Number |
UA @ lucian @ c:irua:96554 |
Serial |
3792 |
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Author |
Roelandts, T.; Batenburg, K.J.; Biermans, E.; Kübel, C.; Bals, S.; Sijbers, J. |
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Title |
Accurate segmentation of dense nanoparticles by partially discrete electron tomography |
Type |
A1 Journal article |
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Year |
2012 |
Publication |
Ultramicroscopy |
Abbreviated Journal |
Ultramicroscopy |
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Volume |
114 |
Issue |
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Pages |
96-105 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT); Vision lab |
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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. |
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Corporate Author |
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Publisher |
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Place of Publication |
Amsterdam |
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Language |
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Wos |
000301954300011 |
Publication Date |
2012-01-06 |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0304-3991; |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
2.843 |
Times cited |
34 |
Open Access |
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Notes |
Fwo |
Approved |
Most recent IF: 2.843; 2012 IF: 2.470 |
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Call Number |
UA @ lucian @ c:irua:97710 |
Serial |
52 |
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