With the advent of in vivo laser scanning fluorescence microscopy techniques, time-series and three-dimensional volumes of living tissue and vessels at micron scales can be acquired to firmly analyze vessel architecture and blood flow. Analysis of a large number of image stacks to extract architecture and track blood flow manually is cumbersome and prone to observer bias. Thus, an automated framework to accomplish these analytical tasks is imperative. The first initiative toward such a framework is to compensate for motion artifacts manifest in these microscopy images. Motion artifacts in in vivo microscopy images are caused by respiratory motion, heart beats, and other motions from the specimen. Consequently, the amount of motion present in these images can be large and hinders further analysis of these images. In this article, an algorithmic framework for the correction of time-series images is presented. The automated algorithm is comprised of a rigid and a nonrigid registration step based on shape contexts. The framework performs considerably well on time-series image sequences of the islets of Langerhans and provides for the pivotal step of motion correction in the further automatic analysis of microscopy images.