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Color deconvolution for python cf : A. C. Ruifrok and D. A. Johnston, “Quantification of histochemical staining by color deconvolution.,” Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology, vol. 23, no. 4, pp. 291–9, Aug. 2001.
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def convert_to_optical_densities(rgb,r0,g0,b0): | |
OD = rgb.astype(float) | |
OD[:,:,0] /= r0 | |
OD[:,:,1] /= g0 | |
OD[:,:,2] /= b0 | |
return -np.log(OD) | |
def color_deconvolution(rgb,r0,g0,b0,verbose=False): | |
stain_OD = np.asarray([[0.18,0.20,0.08],[0.01,0.13,0.0166],[0.10,0.21,0.29]]) #hematoxylin, eosyn, DAB | |
n = [] | |
for r in stain_OD: | |
n.append(r/norm(r)) | |
normalized_OD = np.asarray(n) | |
D = inv(normalized_OD) | |
OD = convert_to_optical_densities(rgb,r0,g0,b0) | |
ODmax = np.max(OD,axis=2) | |
plt.figure() | |
plt.imshow(ODmax>.1) | |
# reshape image on row per pixel | |
rOD = np.reshape(OD,(-1,3)) | |
# do the deconvolution | |
rC = np.dot(rOD,D) | |
#restore image shape | |
C = np.reshape(rC,OD.shape) | |
#remove problematic pixels from the the mask | |
ODmax[np.isnan(C[:,:,0])] = 0 | |
ODmax[np.isnan(C[:,:,1])] = 0 | |
ODmax[np.isnan(C[:,:,2])] = 0 | |
ODmax[np.isinf(C[:,:,0])] = 0 | |
ODmax[np.isinf(C[:,:,1])] = 0 | |
ODmax[np.isinf(C[:,:,2])] = 0 | |
return (ODmax,C) |
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Line 9: What is the expected range of rgb channels (0-255 or 0.-1.)?