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To obtain a coarse alignment of the brain images using the harmonic signals only and the pixel-based registration of SHG/THG image sequences, we used the RecursiveReg Matlab script for Imaris provided by Michael Liebling (UCSB, California, USA) (Thévenaz et al., 1998) without allowing deformation ('rigid body' option).
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Based on this combination of descriptor and similarity measure we have designed a coarse alignment algorithm that eliminates the need to find a group of valid correspondences (which is necessary in most algorithms, including spin images [8]).
In the fourth column of Table 1, the proposed algorithm is used as a coarse alignment algorithm and the G-ICP is used as a fine alignment algorithm.
This is a time-domain approach and exhibits a stable performance independently of the actual STO, making it very well suited as a coarse alignment algorithm.
After a coarse alignment from cross-correlation, one proceeds in computing an initial 3-D reconstruction.
The proposed algorithm can be used as a coarse alignment algorithm.
Some ICP-based approaches have an initial guess or a coarse alignment algorithm to speed up the ICP algorithm.
Figure 7 shows all the steps that must be followed in order to obtain a coarse alignment between two point clouds.
First, we used the SHG/THG image data and automated pixel-based registration [RecursiveReg Matlab script for Imaris (Thévenaz et al., 1998); Fig. S5] to obtain coarse alignment of the brain images.
The increased complexity of a multiple-view coarse alignment has been recognized in [1] with a solution proposed by model graphs and visibility consistency tests.
This effectively extends the applicability of automated coarse alignment to a greater number of less constrained acquisition paths.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com