H&E Histology Color Normalization

This site provides instruction for normalizing H&E stained histology images.
The approach, developed by Dr. Mark Zarella, Chan Yeoh, Dr. David Breen, and Dr. Fernando Garcia, is detailed in "An alternative reference space for H&E color normalization."
The documentation was produced, under the guidance of Dr. Mark Zarella, by Jason DeFuria and Laurence Liss as part of Drexel's 2018 REThink CS program.


These instructions intend to serve as a basis for use of the color normalization functions in the hopes that they might be useful in diagnostic settings.
The Python versions of these programs are a work in progress and are not production ready at this time.




QuPath Integration  QuPath - Open Source Digital Pathology

We have created a workflow, in which color normalization will be run from QuPath, calling on MatLab to do the calculations and corrections.


Quick Usage Example in MatLab

>> sampleImage = imread('/path/to/histology/sample_image.tif')
    >> fullImage = imread('/path/to/histology/full_image.tif')
    >> [structureMap, lumen, nuclei, stroma, cytoplasm] = colorassign_manual(loadedImage)
    >> classifier = train_classifier(loadedImage, structureMap, lumen, nuclei, stroma, cytoplasm)
    >> classified_image = color_classify(fullImage, classifier)
    >> load('target.mat')
    >> normalizedImage = color_normalize(fullImage, target, classified)
    >> imwrite(normalizedImage, '/path/to/output/normalized.tif')


National Science Foundation

REThink CS @ Drexel is based upon work supported by the National Science Foundation under Grant No. CNS-1711773.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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