A Complex Network Approach for Nanoparticle Agglomeration Analysis in Nanoscale Images
Bruno Brandoli, Leonardo Scabini, Jonathan Orue, Mauro Arruda, and Diogo Goncalves, Wesley Goncalves, Raphaell Moreira, Jose, Rodrigues-Jr

TL;DR
This paper presents a novel complex network-based method for analyzing nanoparticle agglomeration in nanoscale images, enabling more insightful interpretation of particle clustering in microscopy data.
Contribution
The paper introduces a new approach that models nanoparticle images as complex networks to detect and analyze agglomerates, improving interpretation over traditional methods.
Findings
Effective detection of nanoparticle agglomerates in STM images
Applicable to both convex and non-convex agglomerates
Enhanced separability of particles in various settings
Abstract
Complex networks have been widely used in science and technology because of their ability to represent several systems. One of these systems is found in Biochemistry, in which the synthesis of new nanoparticles is a hot topic. However, the interpretation of experimental results in the search of new nanoparticles poses several challenges. This is due to the characteristics of nanoparticles images and due to their multiple intricate properties; one property of recurrent interest is the agglomeration of particles. Addressing this issue, this paper introduces an approach that uses complex networks to detect and describe nanoparticle agglomerates so to foster easier and more insightful analyses. In this approach, each detected particle in an image corresponds to a vertice and the distances between the particles define a criterion for creating edges. Edges are created if the distance is…
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Taxonomy
TopicsCell Image Analysis Techniques · Molecular Communication and Nanonetworks
