  Large electron microscopy image datasets for connectomics are typically
composed of thousands to millions of partially overlapping two-dimensional
images (tiles), which must be registered into a coherent volume prior to
further analysis. A common registration strategy is to find matching features
between neighboring and overlapping image pairs, followed by a numerical
estimation of optimal image deformation using a so-called solver program.
  Existing solvers are inadequate for large data volumes, and inefficient for
small-scale image registration.
  In this work, an efficient and accurate matrix-based solver method is
presented. A linear system is constructed that combines minimization of
feature-pair square distances with explicit constraints in a regularization
term. In absence of reliable priors for regularization, we show how to
construct a rigid-model approximation to use as prior. The linear system is
solved using available computer programs, whose performance on typical
registration tasks we briefly compare, and to which future scale-up is
delegated. Our method is applied to the joint alignment of 2.67 million images,
with more than 200 million point-pairs and has been used for successfully
aligning the first full adult fruit fly brain.
