Commit aa305147 authored by Turnhout, M.C. van's avatar Turnhout, M.C. van
Browse files

add very recent Landini ref

parent 726084a9
......@@ -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.\\
\noindent A ImageJ plugin (based on original code made available by A.C.\ Ruifrok) was released in 2004 by Gabriel Landini \cite{Landini2004,Landini2020}. 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 \cite{Landini2020,Landini2020a}. Things such as pure dye identification and image reconstructing have been implemented in this (Java) plugin.
......@@ -178,9 +178,12 @@ For the analysis, it is best when this invented third colour is `perpendicular'
\begin{equation}
\col{k}_3 = \col{\hat{k}}_1 \times \col{\hat{k}}_2 = \begin{bmatrix} k_{G_1}k_{B_2} - k_{B_1}k_{G_2}\\ % Kb*kg - Kg*kb
k_{B_1}k_{R_2} - k_{R_1}k_{B_2} \\ % Kr*kb - Kb*kr,
k_{R_1}k_{G_2} - k_{G_1}k_{R_2} \end{bmatrix} %Kg*kr - Kr*kg
k_{R_1}k_{G_2} - k_{G_1}k_{R_2} \end{bmatrix} \label{anglecross}%Kg*kr - Kr*kg
\end{equation}
Note that this column still needs to be normalized to $\col{\hat{k}}_3$. Also note that you can find the angle between two columns $n$ and $m$ by taking the inverse cosine of the inner dot product of the two columns (when the columns are normalised):
\begin{equation}
\varphi_{nm} =\acos\left(\colt{\hat{k}}_n \cdot \col{\hat{k}}_m\right) \label{angledot}
\end{equation}
Note that this column still needs to be normalized to $\col{\hat{k}}_3$.
\subsection{Summary: colour deconvolution in RGB}
......
......@@ -149,4 +149,34 @@ and the public domain program NIH image.},
timestamp = {2020-10-24},
}
@Article{Haub2015,
author = {Haub, Peter and Meckel, Tobias},
title = {A model based survey of colour deconvolution in diagnostic brightfield microscopy: Error estimation and spectral consideration},
journal = {Scientific reports},
year = {2015},
volume = {5},
pages = {12096},
month = {July},
abstract = {Colour deconvolution is a method used in diagnostic brightfield microscopy to transform colour images of multiple stained biological samples into images representing the stain concentrations. It is applied by decomposing the absorbance values of stain mixtures into absorbance values of single stains. The method assumes a linear relation between stain concentration and absorbance, which is only valid under monochromatic conditions. Diagnostic applications, in turn, are often performed under polychromatic conditions, for which an accurate deconvolution result cannot be achieved. To show this, we establish a mathematical model to calculate non-monochromatic absorbance values based on imaging equipment typically used in histology and use this simulated data as the ground truth to evaluate the accuracy of colour deconvolution. We show the non-linear characteristics of the absorbance formation and demonstrate how it leads to significant deconvolution errors. In particular, our calculations reveal that polychromatic illumination causes 10-times higher deconvolution errors than sequential monochromatic LED illumination. In conclusion, our model can be used for a quantitative assessment of system components - and also to assess and compare colour deconvolution methods.},
doi = {10.1038/srep12096},
file = {[Haub2015]_A Model based Survey of Colour Deconvolution in Diagnostic Brightfield Microscopy Error Estimation and Spectral Consideration.pdf:[Haub2015]_A Model based Survey of Colour Deconvolution in Diagnostic Brightfield Microscopy Error Estimation and Spectral Consideration.pdf:PDF},
pmid = {26223691},
timestamp = {2020-10-25},
}
@Article{Landini2020a,
author = {Landini, Gabriel and Martinelli, Giovanni and Piccinini, Filippo},
title = {Colour Deconvolution--stain unmixing in histological imaging},
journal = {Bioinformatics},
year = {2020},
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.
Availability and implementation
Colour deconvolution is presented here in two open-source forms: a MATLAB program/function and an ImageJ plugin written in Java. Both versions run in Windows, Macintosh, and UNIX-based systems under the respective platforms. Source code and further documentation are available at: https://blog.bham.ac.uk/intellimic/g-landini-software/colour-deconvolution-2/},
doi = {10.1093/bioinformatics/btaa847},
timestamp = {2020-10-25},
}
@Comment{jabref-meta: databaseType:bibtex;}
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