MCMC-Based Algorithm to Adjust Scale Bias in Large Series of Electron Microscopical Ultrathin Sections

摘要

When using a non-rigid registration scheme, it is possible that bias is introduced during the registration process of consecutive sections. This bias can accumulate when large series of sections arc; to be registered and can cause substantial distortions of the scale space of individual sections thus leading to significant measurement bias. This paper presents an automated scheme based on Markov Chain Monte Carlo (MCMC) techniques to estimate and eliminate registration bias. For this purpose, a hierarchical model is used based on the assumption that (a) each section has the same, independent probability to be deformed by the sectioning and therefore the subsequent registration process and (b) the varying bias introduced by the registration process has to be balanced such that the average section area is preserved forcing the average scale parameters to have a mean value of 1.0.