Colour deconvolution: stain unmixing in histological imaging

TLDR

Microscopy images of stained cells and tissues are central to biomedical experiments and routine histopathology, and digital storage enables numerical processing of colour distribution, including colour deconvolution that separates RGB images into dye‑specific absorbance and transmittance channels. The study presents colour deconvolution in two open‑source implementations—a MATLAB function and an ImageJ plugin—to enable morphological and histochemical segmentation, marker localisation, and image enhancement. Both implementations run on Windows, Macintosh, and UNIX‑based systems. Source code, documentation, and supplementary data are publicly available at the provided URLs.

Abstract

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.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/.Supplementary data are available at Bioinformatics online.