Computational alignment methods application to biological real samples

In this paper, we review alignment techniques based on two statistical methods and Fiducial Markers. The aim of this overview is to highlight the advantages and the disadvantages of each method and to enhance the accuracy of alignment assessment. We use real biological TEM tilt series in different modes, namely scanning TEM (STEM) mode and Energy Filtered TEM (EFTEM) mode. Image registration is the process of aligning two or more images of the same scene taken at different times, from different viewpoints and/or by different sensors. Image registration is a crucial step in imaging problems where the valuable information is contained in more than one image. Accurate image alignment is needed for computing three-dimensional reconstructions from transmission electron microscope tilt series. Tilt series are commonly used in electron tomography as a means of collecting three-dimensional information from two-dimensional projections. A common problem encountered is the projection alignment prior to 3D reconstruction. Current alignment techniques usually employ gold particles or image derived markers to correctly align the images. When these markers are not present, correlation or mutual information metrics between adjacent views is used to align them. However, sequential pair wise correlation is prone to bias and the resulting alignment is not always optimal.