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Colour deconvolution: stain unmixing in histological imaging
Author(s) -
Gabriel Landini,
Giovanni Martinelli,
Filippo Piccinini
Publication year - 2020
Publication title -
bioinformatics
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btaa847
Subject(s) - deconvolution , computer science , rgb color model , plug in , blind deconvolution , source code , software , artificial intelligence , segmentation , computer vision , computer graphics (images) , algorithm , programming language
Microscopy images of stained cells and tissues play a central role in most biomedical experiments and routine histopathology. Storing colour histological images digitally opens the possibility to process numerically colour distribution and intensity to extract quantitative data. Among those numerical procedures are colour deconvolution, which enable decomposing an RGB image into channels representing the optical absorbance and transmittance of the dyes when their RGB representation is known. Consequently, a range of new applications become possible for morphological and histochemical segmentation, automated marker localization and image enhancement.

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