Nano1D: An accurate Computer Vision software for analysis and segmentation of low-dimensional nanostructures
Ehsan Moradpur-Tari (1), Sergei Vlassov (1,2), Sven Oras (1,2), Mart, Ernits (1), Elyad Damerchi (1), Boris Polyakovc (3), Andreas Kyritsakis (1),, and Veronika Zadin (1) ((1) Institute of Technology, University of Tartu,, Nooruse 1, 50411 Tartu, Estonia (2) Institute of Physics

TL;DR
Nano1D is a physics-based software that accurately segments and analyzes overlapping 1D nanostructures in microscopy images, outperforming existing models especially in handling overlaps with over 99% accuracy.
Contribution
The paper introduces Nano1D, a novel computational model for precise segmentation and geometrical analysis of overlapping 1D nanostructures in microscopy images.
Findings
Achieves over 99% accuracy in segmenting overlapping nanostructures.
Effectively analyzes geometrical features like length and diameter.
Operates reliably regardless of object size, density, or orientation.
Abstract
Nanoparticles in microscopy images are usually analyzed qualitatively or manually and there is a need for autonomous quantitative analysis of these objects. In this paper, we present a physics-based computational model for accurate segmentation and geometrical analysis of one-dimensional deformable overlapping objects from microscopy images. This model, named Nano1D, has four steps of preprocessing, segmentation, separating overlapped objects and geometrical measurements. The model is tested on SEM images of Ag and Au nanowire taken from different microscopes, and thermally fragmented Ag nanowires transformed into nanoparticles with different lengths, diameters, and population densities. It successfully segments and analyzes their geometrical characteristics including lengths and average diameter. The function of the algorithm is not undermined by the size, number, density, orientation…
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Taxonomy
TopicsImage Processing Techniques and Applications · Cell Image Analysis Techniques · Machine Learning in Materials Science
