normalization.py

import argparse
import numpy as np
from PIL import Image


def normalize_staining(img, save_file=None, Io=240, alpha=1, beta=0.15):
    ''' Normalize staining appearence of H&E stained images
    
    Example use:
        see test.py
        
    Input:
        I: RGB input image
        Io: (optional) transmitted light intensity
        
    Output:
        Inorm: normalized image
        H: hematoxylin image
        E: eosin image
    
    Reference: 
        A method for normalizing histology slides for quantitative analysis. M.
        Macenko et al., ISBI 2009
    '''
             
    HERef = np.array([[0.5626, 0.2159],
                      [0.7201, 0.8012],
                      [0.4062, 0.5581]])
        
    maxCRef = np.array([1.9705, 1.0308])
    
    # define height and width of image
    h, w, c = img.shape
    
    # reshape image
    img = img.reshape((-1,3))

    # calculate optical density
    OD = -np.log((img.astype(float)+1)/Io)
    
    # remove transparent pixels
    ODhat = OD[~np.any(OD<beta, axis=1)]
        
    # compute eigenvectors
    eigvals, eigvecs = np.linalg.eigh(np.cov(ODhat.T))
    
    #eigvecs *= -1
    
    #project on the plane spanned by the eigenvectors corresponding to the two 
    # largest eigenvalues    
    That = ODhat.dot(eigvecs[:,1:3])
    
    phi = np.arctan2(That[:,1],That[:,0])
    
    minPhi = np.percentile(phi, alpha)
    maxPhi = np.percentile(phi, 100-alpha)
    
    vMin = eigvecs[:,1:3].dot(np.array([(np.cos(minPhi), np.sin(minPhi))]).T)
    vMax = eigvecs[:,1:3].dot(np.array([(np.cos(maxPhi), np.sin(maxPhi))]).T)
    
    # a heuristic to make the vector corresponding to hematoxylin first and the 
    # one corresponding to eosin second
    if vMin[0] > vMax[0]:
        HE = np.array((vMin[:,0], vMax[:,0])).T
    else:
        HE = np.array((vMax[:,0], vMin[:,0])).T
    
    # rows correspond to channels (RGB), columns to OD values
    Y = np.reshape(OD, (-1, 3)).T
    
    # determine concentrations of the individual stains
    C = np.linalg.lstsq(HE,Y, rcond=None)[0]
    
    # normalize stain concentrations
    maxC = np.array([np.percentile(C[0,:], 99), np.percentile(C[1,:],99)])
    tmp = np.divide(maxC,maxCRef)
    C2 = np.divide(C,tmp[:, np.newaxis])
    
    # recreate the image using reference mixing matrix
    Inorm = np.multiply(Io, np.exp(-HERef.dot(C2)))
    Inorm[Inorm>255] = 254
    Inorm = np.reshape(Inorm.T, (h, w, 3)).astype(np.uint8)  
    
#     # unmix hematoxylin and eosin
#     H = np.multiply(Io, np.exp(np.expand_dims(-HERef[:,0], axis=1).dot(np.expand_dims(C2[0,:], axis=0))))
#     H[H>255] = 254
#     H = np.reshape(H.T, (h, w, 3)).astype(np.uint8)
    
#     E = np.multiply(Io, np.exp(np.expand_dims(-HERef[:,1], axis=1).dot(np.expand_dims(C2[1,:], axis=0))))
#     E[E>255] = 254
#     E = np.reshape(E.T, (h, w, 3)).astype(np.uint8)
    
    if save_file is not None:
        Image.fromarray(Inorm).save(save_file+'.png')
        # Image.fromarray(H).save(save_file+'_H.png')
        # Image.fromarray(E).save(save_file+'_E.png')

    return Inorm #, H, E
    
    
if __name__=='__main__':
    
    parser = argparse.ArgumentParser()
    parser.add_argument('--imageFile', type=str, default='example1.tif', help='RGB image file')
    parser.add_argument('--saveFile', type=str, default='output', help='save file')
    parser.add_argument('--Io', type=int, default=240)
    parser.add_argument('--alpha', type=float, default=1)
    parser.add_argument('--beta', type=float, default=0.15)
    args = parser.parse_args()
    
    img = np.array(Image.open(args.imageFile))

    normalize_staining(img = img,
                      save_file = args.saveFile,
                      Io = args.Io,
                      alpha = args.alpha,
                      beta = args.beta)