Article ; Online: Joint Iterative Fast Projection Matching for Fully Automatic Marker-free Alignment of Nano-tomography Reconstructions.
2020 Volume 10, Issue 1, Page(s) 7330
Abstract: Highly accurate, fully automatic marker-free image alignment plays an important role in nano-tomographic reconstruction, particularly in cases where the spatial resolution of the tomographic system is on the nanometer scale. However, highly accurate ... ...
| Abstract | Highly accurate, fully automatic marker-free image alignment plays an important role in nano-tomographic reconstruction, particularly in cases where the spatial resolution of the tomographic system is on the nanometer scale. However, highly accurate marker-free methods such as the projection matching method are computationally complex and time-consuming. Achieving alignment accuracy with reduced computational complexity remains a challenging problem. In this study, we propose an efficient method to achieve marker-free fully automatic alignment. Our method implements three main alignment procedures. First, the frequency-domain common line alignment method is used to correct the in-plane rotational errors of each projection. Second, real-space common line alignment method is used to correct the vertical errors of the projections. Finally, a single layer joint-iterative reconstruction and re-projection method is used to correct the horizontal projection errors. This combined alignment approach significantly reduces the computational complexity of the classical projection matching method, and increases the rate of convergence towards determining the accurate alignment. The total processing time can be reduced by up to 4 orders of magnitude as compared to the classical projection matching method. This suggests that the algorithm can be used to process image alignment of nano-tomographic reconstructions on a conventional personal computer in a reasonable time-frame. |
|---|---|
| Language | English |
| Publishing date | 2020-04-30 |
| Publishing country | England |
| Document type | Journal Article ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 2615211-3 |
| ISSN | 2045-2322 ; 2045-2322 |
| ISSN (online) | 2045-2322 |
| ISSN | 2045-2322 |
| DOI | 10.1038/s41598-020-62949-1 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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