In this work, we have developed a fast, reliable and unbias algorithm to analyze size distributions and morphological properties of nanoscale images taken by Transmission Electron Microscopy. As the physicochemical properties of nanostructures strongly depend on their size, shape and surface characteristics, it is of great importance to have access to a set of reliable tools to quantify them. Our image processing process is subdivided into three major subroutines: image preprocessing, image analysis and results interpretation. Several algorithms are used to adjust the image contrast as the adapthisteq function based on the contrast-limited adaptive histogram equalization (CLAHE) method. A segmentation procedure, based on watershed transformation, has been implemented and tested. Introducing a simple geometrical criteria the software is able to distinguish between spherical, triangular and hexagonal shapes. The sphericity, roundness and roughness are also quantified. Figure 1 and 2 show the application of the software for different examples. We demonstrate that it is possible to distinguish between alive and dead bacteria by looking at the roundness, surface roughness and the ratio between the 2D projection area of the bacteria and the bounding box area enclosing the bacteria. For the case of nanoparticles, using the fractal dimension, we can predict the reactivity of iron nanoparticles used for environmental remediation.
To cite this abstract:Carlos Arroyo, Alexis Debut, Andrea Vaca, Brajesh Kumar, Luis Cumbal; Image processing tools for morphological analysis of nanoscale objects. The 16th European Microscopy Congress, Lyon, France. https://emc-proceedings.com/abstract/image-processing-tools-for-morphological-analysis-of-nanoscale-objects/. Accessed: May 24, 2019
EMC Abstracts - https://emc-proceedings.com/abstract/image-processing-tools-for-morphological-analysis-of-nanoscale-objects/