Modifications:

• H&E normalization optimized for speed and multiprocessing - June '19 Joni Juvonen
• Speed optimization (~8x speed improvements) - June '19 Mikko
• H&E Stain augmentor - August '19 Joni Juvonen

Randomize staining appearance for H&E stained images.

Example usage:

import stainAugmentation
import cv2

original_image = cv2.cvtColor(cv2.imread('imgs/example1.png'), cv2.COLOR_BGR2RGB)
augmented_image = stainAugmentation.randomHEStaining(original_image)

import PIL
import numpy as np
import stainAugmentation

def _HEaugment(x):
    img = PIL.Image.fromarray(image2np(x*255).astype('uint8'))
    x = stainAugmentation.randomHEStaining(img)
    x = pil2tensor(x/255.,np.float32)
    return x

HEaugment = TfmPixel(_HEaugment)

Now, HEaugment() can be passed to transformations list: tfms = [HEaugment()]

NOTE: Originally published at the Assessment of Mitosis Detection Algorithms 2013 challenge website (http://http://amida13.isi.uu.nl).

Adapted from original implementation (https://github.com/mitkovetta/staining-normalization).

One of the major difficulties in histopathology image analysis is apperance variability. For example, when performing mitosis detection, many false positives can arise when the histopathology slide is overstained. This Python code, adapted from the original MATLAB implementation, performs staining unmixing (separation of the hematoxylin and eosing stains) and apperance normalization. It is based on the method described in [1]. Some examples of staining normalization can be seen in the figure below.

[1] A method for normalizing histology slides for quantitative analysis, M Macenko, M Niethammer, JS Marron, D Borland, JT Woosley, G Xiaojun, C Schmitt, NE Thomas, IEEE ISBI, 2009. dx.doi.org/10.1109/ISBI.2009.5193250

First, compile cython binaries: run python ./setup.py build_ext --inplace