Commit 7c828ec9 authored by Turnhout, M.C. van's avatar Turnhout, M.C. van
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parent 847dfc37
...@@ -12,7 +12,7 @@ They use the law of Bouguer-Lambert-Beer \cite{Beer1852,Perrin1948} for a linear ...@@ -12,7 +12,7 @@ They use the law of Bouguer-Lambert-Beer \cite{Beer1852,Perrin1948} for a linear
Ruifrok and Johnston only sketch an (analytical) outline of the procedure and do no discuss the practical implementation, or how to `reconstruct' images with the estimated dye amounts.\\ Ruifrok and Johnston only sketch an (analytical) outline of the procedure and do no discuss the practical implementation, or how to `reconstruct' images with the estimated dye amounts.\\
\noindent A ImageJ plugin (based on original code made available by A.C.\ Ruifrok) was released in 2004 by Gabriel Landini \cite{Landini2004,Landini2020} and (very) recently updated \cite{Landini2020,Landini2020a}. Things such as pure dye identification and image reconstructing have been implemented in this (Java) plugin. \noindent A ImageJ plugin (based on original code made available by A.C.\ Ruifrok) was released in 2004 by Gabriel Landini \cite{Landini2004,Landini2020} and (very) recently updated by Landini, Martinelli and Piccini \cite{Landini2020a}. Things such as pure dye identification and image reconstructing have been implemented in this (Java) plugin.
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...@@ -87,7 +87,7 @@ and the public domain program NIH image.}, ...@@ -87,7 +87,7 @@ and the public domain program NIH image.},
@Misc{Landini2020, @Misc{Landini2020,
author = {Landini, G.}, author = {Landini, G.},
title = {Novel context-based segmentation algorithms for intelligent microscopy}, title = {Novel context-based segmentation algorithms for intelligent microscopy},
howpublished = {\href{https://blog.bham.ac.uk/intellimic/g-landini-software/colour-deconvolution-2/}{https://blog.bham.ac.uk/intellimic/g-landini-software/colour-deconvolution-2/}}, howpublished = {\href{https://blog.bham.ac.uk/intellimic/g-landini-software/colour-deconvolution/}{https://blog.bham.ac.uk/intellimic/g-landini-software/colour-deconvolution/}},
year = {2020}, year = {2020},
timestamp = {2020-09-26}, timestamp = {2020-09-26},
} }
...@@ -169,6 +169,7 @@ and the public domain program NIH image.}, ...@@ -169,6 +169,7 @@ and the public domain program NIH image.},
title = {Colour Deconvolution--stain unmixing in histological imaging}, title = {Colour Deconvolution--stain unmixing in histological imaging},
journal = {Bioinformatics}, journal = {Bioinformatics},
year = {2020}, year = {2020},
note = {See also \href{https://blog.bham.ac.uk/intellimic/g-landini-software/colour-deconvolution-2/}{https://blog.bham.ac.uk/intellimic/g-landini-software/colour-deconvolution-2/}},
abstract = {Motivation abstract = {Motivation
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 is colour deconvolution, which enables 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 localisation and image enhancement. 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 is colour deconvolution, which enables 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 localisation and image enhancement.
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