Color deconvolution was introduced by Ruifrok et al. in 2001 [1] and describes a method to extract stain intensities from RGB images of histological slides. Color deconvolution is widely used for image processing in histology and there are very efficient Fiji and Python implementation available (see below, [2]). This repository provides an efficient Matlab implementation of color deconvolution. The code is partly based on the python implementation in scikit-image.

Panels: Original - Hematoxylin - DAB - residual

The source image is stained with Hematoxylin and DAB. The third channel represents the residual and should be empty. Here, the standard values for the deconvolution matrix fit pretty well and the residual is small.

The output is similar to Fiji's output (note that the contrast is enhanced by stretching the histograms). The residual's histogram is also stretched and it can be appreciated that the residual is essentially random noise, so it does not contain a lot of information.

• fix contrast scaling in inverse color deconvolution function (RecombineStains)

• Fiji implementation explained on the Fiji website
• python scikit-image implementation on GitHub
• in-depth explanation of the fiji implementation on mecourse.com
• another Matlab implementation by Antony Chan on web.hku.hk (slower because of two nested for loops)
• yet another Matlab implementation: imagenebula

[1] Ruifrok AC, Johnston DA. Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histol. 2001 Aug;23(4) 291-299. PubMed PMID: 11531144.

[2] van der Walt S, Schönberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, Gouillart E, Yu T, scikit-image contributors. scikit-image: image processing in Python. PeerJ. 2014;2 e453. doi:10.7717/peerj.453. PubMed PMID: 25024921; PubMed Central PMCID: PMC4081273.

[3] Pontén F, Jirström K, Uhlen M. The Human Protein Atlas--a tool for pathology. J Pathol. 2008 Dec;216(4) 387-393. doi:10.1002/path.2440. PubMed PMID: 18853439.