accurate-detection-of-spherical-objects-in-a-complex-background
https://github.com/ursgasser/accurate-detection-of-spherical-objects-in-a-complex-background
Science Score: 44.0%
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- Host: GitHub
- Owner: ursgasser
- License: other
- Language: IDL
- Default Branch: main
- Size: 78.8 MB
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Metadata Files
README.md
Accurate-detection-of-spherical-objects-in-a-complex-background
IDL code for the detection of spherical particles in 3d microscopy images. This code was published in (see citation.bib) U. Gasser and B. Zhou, OpticsExpress, DOI: https://doi.org/10.1364/OE.434652 (2021).
We plan to also give a python version.
The particle detection code is given in the files findspheresdiv.pro and findspheresy2.pro.
findspheresdiv.pro: B&gradient method explained in the paper mentioned above.
findspheresy2.pro: B&Y method explained in the paper mentioned above.
See the IDL code in the files 'img4769example.txt' and 'img4400example.txt' for particle-detection examples. Calculated images are used in these two examples and the determined positions can be compared with the known positions of the spheres. Both the B&$\nabla$ and B&Y methods are used with and without fracshift and suppression of bridges to compare methods. An example of the B&Y method with a measured confocal image is given in file 'imgasm533example.txt'.
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- Login: ursgasser
- Kind: user
- Repositories: 1
- Profile: https://github.com/ursgasser
Citation (citation.bib)
% If you wish to cite the 'find_spheres' package in a publication,
% please use this citation:
@Article{find_spheres_2021,
author = {Gasser, U. and Zhou, B.},
title = {Accurate detection of spherical objects in a complex background},
journal = {Optics Express},
year = 2021,
volume = 29,
number = 23,
pages = {1--18},
doi = {https://doi.org/10.1364/OE.434652}
}
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