A vision-based, 3D reconstruction technique for scanning electron microscopy: direct comparison with atomic force microscopy.

Abstract

High-resolution, detailed 3D reconstructions of biological specimens obtained from scanning electron microscopy stereo-micrographs and proprietary software were compared with Tapping-Mode AFM datasets of the same fields. The reconstruction software implements several original solutions including a neural adaptive point-matching technique, the ability to build an irregular triangulated mesh rather than a regular orthogonal grid, and the ability to re-map one of the original images exactly onto the reconstructed surface. The technique was applied to human nerve tissue to obtain 1,424 x 968-pixel, texture-mapped datasets, which were subsequently compared against 512 x 512-pixel AFM datasets from the same viewfields. Accounting for the inherent differences of the two techniques, direct comparison revealed an excellent visual match. The correspondence was also quantified by calculating the cross-correlation coefficient between corresponding altimetric profiles in SEM and AFM data, which consistently exceeded a figure of 0.9, with a rate of point mismatch in the order of 0.01%. Research is still underway to improve the robustness of the technique when applied to arbitrary images