@proceedings {46793,
	title = {Bead-based mosaicing of single plane illumination microscopy images using geometric local descriptor matching.},
	volume = {7259},
	year = {2009},
	month = {2009 Mar 27},
	pages = {72592S},
	publisher = {SPIE},
	address = {Lake Buena Vista, FL},
	abstract = {<p>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.</p>
},
	doi = {10.1117/12.812612},
	author = {Preibisch, Stephan and Saalfeld, Stephan and Rohlfing,  Torsten and Tomancak, Pavel}
}
