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In electron tomography, alignment accuracy is critical for high-resolution reconstruction. However, the automatic alignment of a tilt series without fiducial markers remains a challenge. Here, we propose a new alignment method based on Scale-Invariant Feature Transform (SIFT) for marker-free alignment. The method covers the detection and localization of interest points (features), feature matching, feature tracking and optimization of projection parameters. The proposed method implements a highly reliable matching strategy and tracking model to detect a huge number of feature tracks. Furthermore, an incremental bundle adjustment method is devised to tolerate noise data and ensure the accurate estimation of projection parameters. Our method was evaluated with a number of experimental data, and the results exhibit an improved alignment accuracy comparable with current fiducial marker alignment and subsequent higher resolution of tomography.
Electron Microscope Tomography, Mitochondria, Liver, Caveolae, Mice, Electron tomography, Fiducial Markers, SIFT, Hepatocytes, Image Processing, Computer-Assisted, Bundle adjustment, Animals, Algorithms, Alignment, Centrioles
Electron Microscope Tomography, Mitochondria, Liver, Caveolae, Mice, Electron tomography, Fiducial Markers, SIFT, Hepatocytes, Image Processing, Computer-Assisted, Bundle adjustment, Animals, Algorithms, Alignment, Centrioles
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 35 | |
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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