Accelerating feature based registration using the Johnson-Lindenstrauss lemma

Abstract. We introduce an efficient search strategy to substantially accelerate feature based registration. Previous feature based registration algorithms often use truncated search strategies in order to achieve small computation times. Our new accelerated search strategy is based on the realization that the search for corresponding features can be dramat-ically accelerated by utilizing Johnson-Lindenstrauss dimension reduc-tion. Order of magnitude calculations for the search strategy we propose here indicate that the algorithm proposed is more than a million times faster than previously utilized naive search strategies, and this advan-tage in speed is directly translated into an advantage in accuracy as the fast speed enables more comparisons to be made in the same amount of time. We describe the accelerated scheme together with a full complex-ity analysis. The registration algorithm was applied to large transmission electron microscopy (TEM) images of neural ultrastructure. Our experi-ments demonstrate that our algorithm enables alignment of TEM images with increased accuracy and efficiency compared to previous algorithms.