Fast stitching of large 3d biological datasets.

In order to study anatomy of organisms with high-resolution there is an increasing demand to image large specimen in three dimensions (3D). Confocal microscopy is able to produce high-resolution 3D images, but these are limited by its relatively small field of view compared to the size of large biological specimens. To overcome this drawback, motorized stages moving the sample are used to create a tiled scan of the whole specimen. The physical coordinates provided by the microscope stage are not precise enough to allow reconstruction (”Stitching”) of the whole image from individual image stacks.
We developed an algorithm, as well as an ImageJ plug-in, based on the Fourier Shift Theorem that computes all possible translations (x, y, z) between two 3D images at once, yielding the best overlap in terms of the cross correlation measure. Apart from the obvious gain in computation time it has the advantage that it cannot be trapped in local minima as it simply computes all possible solutions. Computing the overlap between two adjacent image stacks is fast (12 seconds for two 512x512x89 images on a Intel ® Core2Duo with 2.2GHz) making it suitable for real time use, i.e. computing the output image during acquisition of the individual image stacks.
To compensate the possible shading- and brightness differences we apply a smooth linear intensity transition between the overlapping stacks. Additionally we extended the to generic 3D registration using gradient based rotation detection on top of the phase correlation method. We demonstrate the performance of our 3D stitching plug-in on several tiled confocal images and show an example of its application for 3D registration.