  Field ion microscopy (FIM) allows to image individual surface atoms by
exploiting the effect of an intense electric field. Widespread use of atomic
resolution imaging by FIM has been hampered by a lack of efficient image
processing/data extraction tools. Recent advances in imaging and data mining
techniques have renewed the interest in using FIM in conjunction with automated
detection of atoms and lattice defects for materials characterization. After a
brief overview of existing routines, we review the use of machine learning (ML)
approaches for data extraction with the aim to catalyze new data-driven
insights into high electrical field physics. Apart from exploring various
supervised and unsupervised ML algorithms in this context, we also employ
advanced image processing routines for data extraction from large sets of FIM
images. The outcomes and limitations of such routines are discussed, and we
conclude with the possible application of energy minimization schemes to the
extracted point clouds as a way of improving the spatial resolution of FIM.
