Automated detection of particles, clusters and islands in scanning probe microscopy images
Surf. Sci. , Volume 494 p. 43- 52
In order to obtain quantitative information from scanning tunnelling microscopy (STM) images, image processing and pattern recognition techniques are very valuable tools. We developed an algorithm which automatically determines the positions and sizes of small particles and other nanostructures in STM and atomic force microscopy (AFM) images. This algorithm has been tested and used both in the study of Pd nanoparticles supported on TiO2 and in the study of diffusing In atoms embedded in a Cu surface. First the original STM image is filtered in order to obtain an image of the background. Subtracting this `background' image from the original image eliminates the height variations in the substrate, such as atomic steps. The particles can then be found by discrimination with respect to a threshold height. Once the particles are located, their exact position and size are determined and used for further analysis.
Jak, M.J.J, Konstapel, C, van Kreuningen, A, Verhoeven, J, van Gastel, R, & Frenken, J.W.M. (2001). Automated detection of particles, clusters and islands in scanning probe microscopy images. Surf. Sci., 494, 43–52.