Macenko stain normalization deteriorate images with bad staining

[mostafajahanifar]

• TIA Toolbox version: 1.0
• Python version: 3.8
• Operating System: Windows

Description

When traying Macenko stain normalization to normalize images with that don't have perfect staining in first place, the normalization process would deteriorate the output quality. In particular, it seems that division by maxC_source is causing it:

maxC_source = np.percentile(source_concentrations, 99, axis=0).reshape((1, 2))
source_concentrations *= self.maxC_target / maxC_source

It seems that returned maxC_source is not favorable when we don't have enough stain variation in the source image.

What I Did

Consider the the target image:

and the source image:

this will be the normalized source which is clearly deteriorated:

the code to reproduce this:

from tiatoolbox.utils.misc import imread, imwrite
from tiatoolbox.tools.stainnorm import MacenkoNormalizer
source= imread('D:/source.png') 
target = imread('D:/target.png') 
normalizer = MacenkoNormalizer()
normalizer.fit(target)
source_normalized = normalizer.transform(source)

The similar problem exists with Macenko normalizer in staintools:

the code to reproduce this:

import staintools

# Read data
target = staintools.read_image("/root/workspace/target.png")
to_transform = staintools.read_image("/root/workspace/source.png")

# Standardize brightness (optional, can improve the tissue mask calculation)
target = staintools.LuminosityStandardizer.standardize(target)
to_transform = staintools.LuminosityStandardizer.standardize(to_transform)

# Stain normalize
normalizer = staintools.StainNormalizer(method='macenko')
normalizer.fit(target)
transformed = normalizer.transform(to_transform)

However, if you use Vahedane normalizer, the output would look desirable like this:

[mostafajahanifar]

This seems to be an intrinsic behavior from Macenko algorithm which happens when the source/target image does not show much stain variation in it. Therefore, I'm closing this issue.