Single Plane Illumination Microscopy (SPIM) is an emerging microscopic technique that enables live imaging of large
biological specimens in their entirety. By imaging the biological sample from multiple angles, SPIM has the potential to
achieve isotropic resolution throughout relatively large biological specimens. For every angle, however, only a shallow
section of the specimen is imaged with high resolution, whereas deeper regions appear increasingly blurred. Existing
intensity-based registration techniques still struggle to robustly and accurately align images that are characterized by limited
overlap and/or heavy blurring. To be able to register such images, we add sub-resolution fluorescent beads to the rigid
agarose medium in which the imaged specimen is embedded. For each segmented bead, we store the relative location
of its n nearest neighbors in image space as rotation-invariant geometric local descriptors. Corresponding beads between
overlapping images are identified by matching these descriptors. The bead correspondences are used to simultaneously
estimate the globally optimal transformation for each individual image. The final output image is created by combining
all images in an angle-independent output space, using volume injection and local content-based weighting of contributing
images. We demonstrate the performance of our approach on data acquired from living embryos of Drosophila and fixed
adult C.elegans worms. Bead-based registration outperformed intensity-based registration in terms of computation speed
by two orders of magnitude while producing bead registration errors below 1 μm (about 1 pixel). It, therefore, provides
an ideal tool for processing of long term time-lapse recordings of embryonic development consisting of hundreds of time points.
Single Plane Illumination Microscopy (SPIM; Huisken et al., Nature 305(5686):1007-1009, 2004) is an emerging microscopic
technique that enables live imaging of large biological specimens in their entirety. By imaging the living biological
sample from multiple angles SPIM has the potential to achieve isotropic resolution throughout even relatively large biological
specimens. For every angle, however, only a relatively shallow section of the specimen is imaged with high resolution,
whereas deeper regions appear increasingly blurred. In order to produce a single, uniformly high resolution image, we
propose here an image mosaicing algorithm that combines state of the art groupwise image registration for alignment with
content-based image fusion to prevent degrading of the fused image due to regional blurring of the input images. For
the registration stage, we introduce an application-specific groupwise transformation model that incorporates per-image as
well as groupwise transformation parameters. We also propose a new fusion algorithm based on Gaussian filters, which is
substantially faster than fusion based on local image entropy. We demonstrate the performance of our mosaicing method
on data acquired from living embryos of the fruit fly, Drosophila, using four and eight angle acquisitions.
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