Nano1D: An accurate Computer Vision software for analysis and segmentation of low-dimensional nanostructures

• URL: http://arxiv.org/abs/2306.15319v3
• Date: Sun, 10 Dec 2023 19:46:02 GMT
• Title: Nano1D: An accurate Computer Vision software for analysis and segmentation of low-dimensional nanostructures
• Authors: 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, University of Tartu, W. Ostwaldi 1, 50411 Tartu, Estonia (3) Institute of Solid State Physics, University of Latvia, Kengaraga street 8, LV-1063 Riga, Latvia)
• Abstract summary: The model, named Nano1D, has four steps of preprocessing, segmentation, separating overlapped objects and geometrical measurements. It successfully segments and analyzes their geometrical characteristics including lengths and average diameter. The main strength of the model is shown to be its ability to segment and analyze overlapping objects successfully with more than 99% accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 and overlapping of objects in images. The main strength of the model is shown to be its ability to segment and analyze overlapping objects successfully with more than 99% accuracy, while current machine learning and computational models suffer from inaccuracy and inability to segment overlapping objects. Benefiting from a graphical user interface, Nano1D can analyze 1D nanoparticles including nanowires, nanotubes, nanorods in addition to other 1D features of microstructures like microcracks, dislocations etc.

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