A Probabilistic Template Model for Finding Macromolecules in MET Volume Images

摘要

We introduce and investigate probabilistic templates with particular focus on the application of protein identification in electron tomography volumes. We suggest to create templates with a weighted averaging operation of several object instances after alignment of an identified subpart. The subpart to be aligned should, ideally, correspond to a rigid and easily identifiable part of the object. The proposed templates enables common rigid template matching methods to also find different shape variations without increasing time complexity in the actual search procedure, since a static template is still used. We present general ideas on how to perform the object instance alignment and look specifically at how to do it for the antibody macromolecule IgG.