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.

Generalized Additive Models (GAMs) with age, period and cohort as possible covariates are used to predict future mortality improvements for the Irish population. The GAMs considered are the 1-dimensional age + period and age + cohort models and the 2-dimensional age-period and age-cohort models. In each case thin plate regression splines are used as the smoothing functions. The generalized additive models are compared with the P-Spline (Currie et al., 2004) and Lee-Carter (Lee & Carter, 1992) models included in version 1.0 of the Continuous Mortality Investigation (CMI) library of mortality projections. Using the Root Mean Square Error to assess the accuracy of future predictions, the GAMs outperform the P-Spline and Lee-Carter models over intervals of 25 and 35 years in the age range 60 to 90. The GAMs allow intuitively simple models of mortality to be specified whilst also providing the flexibility to model complex relationships between the covariates. The majority of morality improvements derived from the projections of future Irish mortality yield annuity values at ages 60, 65, 70 and 80 in 2007 in the range of annuity values calculated, assuming a 2 to 4 percent annual compound improvement in mortality rates for both males and females.