Computer-readable Image Markers for Automated Registration in Correlative Microscopy - "autoCRIM"

J Sheriff; I W Fletcher; P J Cumpson

doi:10.1016/j.ultramic.2021.113322

Computer-readable Image Markers for Automated Registration in Correlative Microscopy - "autoCRIM"

Ultramicroscopy. 2021 Sep:228:113322. doi: 10.1016/j.ultramic.2021.113322. Epub 2021 May 24.

Authors

J Sheriff¹, I W Fletcher², P J Cumpson³

Affiliations

¹ School of Engineering, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK. Electronic address: j.sheriff2@newcastle.ac.uk.
² School of Engineering, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK.
³ Mark Wainwright Analytical Centre, UNSW, Sydney NSW 2052, Australia.

PMID: 34098347
DOI: 10.1016/j.ultramic.2021.113322

Abstract

We present a newly developed methodology using computer-readable fiducial markers to allow images from multiple imaging modalities to be registered automatically. This methodology makes it possible to correlate images from many surface imaging techniques to provide an unprecedented level of surface detail on a nanometre scale that no one technique can provide alone. This methodology provides the capability to navigate to specific areas of interest when transferring samples from machine to machine seamlessly. Then taking data acquired from scanning electron microscope (SEM), secondary ion mass spectrometry (SIMS), x-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM) and optical inspection tools and combining all the data acquired to then generate a 3D data representative model of a surface.

Keywords: Automation; Computer Readable Image Marker (CRIM); Correlative Microscopy; Image Alignment; Image Registration; Surface Analysis.

Publication types

Research Support, Non-U.S. Gov't