Difference between revisions of "LUMoS Spectral Unmixing"

(Adding citation to our paper about this which recently went up on biorxiv)
(Adding infobox)
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{{Infobox
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| software              = ImageJ/Fiji
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| name                  = LUMoS Spectral Unmixing
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| author                = Tristan McRae
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| maintainer            = Tristan McRae
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| released              = June 2019
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| source                = [https://github.com/tristan-mcrae-rochester/Multiphoton-Image-Analysis/blob/master/Spectral%20Unmixing/Code/ImageJ-FIJI/LUMoS_Spectral_Unmixing.java GitHub]
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| category              = [[:Category:Plugins|Plugins]], [[:Category:Color processing|Color Processing]], [[:Category:Colocalization|Colocalization]]
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}}
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[[Category:Plugins]]
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[[Category:Color processing]]
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[[Category:Colocalization]]
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Learning Unsupervised Means of Spectra (LUMoS) is a blind spectral unmixing method that uses k-means clustering to separate different features of a fluorescence microscopy image into a multi-channel image in which each output channel corresponds to a single fluorophpore.  
 
Learning Unsupervised Means of Spectra (LUMoS) is a blind spectral unmixing method that uses k-means clustering to separate different features of a fluorescence microscopy image into a multi-channel image in which each output channel corresponds to a single fluorophpore.  
  

Revision as of 08:13, 11 October 2019

LUMoS Spectral Unmixing (ImageJ/Fiji)
Author Tristan McRae
Maintainer Tristan McRae
Source GitHub
Initial release June 2019
Category Plugins, Color Processing, Colocalization

Learning Unsupervised Means of Spectra (LUMoS) is a blind spectral unmixing method that uses k-means clustering to separate different features of a fluorescence microscopy image into a multi-channel image in which each output channel corresponds to a single fluorophpore.

Additional information including a user guide and be found here.


If you use this program in your work, please cite the pertinent paper:

McRae TD, Oleksyn D, Miller J, Gao Y-R. Robust blind spectral unmixing for fluorescence microscopy using unsupervised learning. bioRxiv. 2019; 797993. doi:10.1101/797993