A global optimization approach to image translational alignment in electron microscopy

Electron microscope tomography allows determination of the 3D structure of biological specimens, which is critical to understanding their function. Prior to the 3D reconstruction procedure, the images taken from the microscope have to be properly aligned. Traditional alignment methods in this field are based on a phase residual function to be minimized by inefficient exhaustive search procedures. This work addresses this minimization problem from a global optimization perspective. UEGO, an evolutionary multimodal optimization algorithm, has been applied and evaluated for the task of image alignment in this field. UEGO has turned out to be a promising technique alternative to the standard methodology. The alignments found out by UEGO show high levels of accuracy, while reducing the number of function evaluations by a significant factor with respect to the standard method.