scholarly journals Development of an Algorithm for Automatic Image Detection of Biological Particles in Transmission Electron Microscopy Images

2012 ◽  
Vol 18 (S5) ◽  
pp. 3-4 ◽  
Author(s):  
M. C. Proença ◽  
J. F. Moura Nunes ◽  
A. P. Alves de Matos
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Transmission Electron Microscopy ◽  
High Resolution ◽  
3D Reconstruction ◽  
Image Detection ◽  
Virus Particles ◽  
Transmission Electron ◽  
Automatic Image Processing ◽  
Microscopy Images

Automatic image processing of transmission electron microscopy images (TEM) is a utopia often pursued, considering the thousands of images necessary to ensure a high resolution 3D reconstruction of virus particles or other macromolecular machines.

Characterization of the ZnSe/GaAs Interface Layer by Tem and Spectroscopic Ellipsometry

1992 ◽  
Vol 280 ◽  
Author(s):  
R. Dahmani ◽  
L. Salamanca-Riba ◽  
D. P. Beesabathina ◽  
N. V. Nguyen ◽  
D. Chandler-Horowitz ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Transmission Electron Microscopy ◽  
High Resolution ◽  
Spectroscopic Ellipsometry ◽  
Interface Layer ◽  
Transmission Electron ◽  
Three Phase ◽  
Znse Thin Films ◽  
Microscopy Images

ABSTRACTThe interface between ZnSe thin films and GaAs substrates is characterized by High Resolution Transmission Electron Microscopy and room temperature Spectroscopic Ellipsometry. The films were grown on (001) GaAs by Molecular Beam Epitaxy. A three-phase model is used in the reduction of the ellipsometric data, from which the presence of a transition layer of Ga2Se3, with a thickness of less than 1 nm, is confirmed. These results corroborate the high resolution transmission electron microscopy images obtained from the same samples.

REW– exit-wave reconstruction and alignments for focus-variation high-resolution transmission electron microscopy images

2007 ◽  
Vol 40 (3) ◽  
pp. 614-614 ◽  
Author(s):  
F. Lin ◽  
Q. Chen ◽  
L.-M. Peng
Keyword(s):  
Electron Microscopy ◽  
Transmission Electron Microscopy ◽  
High Resolution ◽  
Maximum Likelihood Method ◽  
Transmission Electron Microscopy Image ◽  
Likelihood Method ◽  
Transmission Electron ◽  
Microscopy Image ◽  
Focus Variation ◽  
Microscopy Images

A Windows-based computer program has been developed for exit-wave reconstruction and experimental high-resolution transmission electron microscopy image alignment. While the exit-wave reconstruction is performed mainly using the maximum-likelihood method, image alignments may be carried out using several algorithms, including the time-consuming but robust genetic algorithm.

scholarly journals Patterns detection in diffraction images of transmission electron microscopy

2020 ◽  
Author(s):  
Stepan Nebaba ◽  
Alexander Pak
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Transmission Electron Microscopy ◽  
Distance Estimation ◽  
Transmission Electron ◽  
Automated Processing ◽  
Material Sample ◽  
Diffraction Patterns ◽  
Automated Algorithms ◽  
Microscopy Images

Specialized software that supports existing approaches to processing images of the crystal structure of materials for analyzing transmission electron microscopy images have a lot of different digital image processing methods, but major part of it are weakly automated. In some tasks automated algorithms of image processing have been developed, e.g. in task of estimation of the width of a layer of material from a raster image. The paper considers the problem of automated processing of diffraction images obtained by transmission electron microscopy. A number of modifications, such as Watershed algorithm, binarization and Fast Fourier Transform, are proposed for existing image processing algorithms. These modifications can help automate the processing of the diffraction pattern of a material sample from an image of transmission electron microscopy. The given examples of image processing of particular cases of diffraction patterns have shown the prospects for the development of algorithm based on combination of the proposed modifications of considered algorithms. Adaptive binarization with Watershed segmentation would be useful in automated distance estimation in transmission electron microscopy images.

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