mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-27 22:08:28 +08:00
Merge pull request #441 from spotter/color_deconv
ENH: Adding color deconvolution for immunohistochemical images.
This commit is contained in:
Stefan van der Walt
@@ -136,3 +136,6 @@
|
||||
|
||||
- Thouis Jones
|
||||
Vectorized operators for arrays of 16-bit ints.
|
||||
|
||||
- Xavier Moles Lopez
|
||||
Color separation (color deconvolution) for several stainings.
|
||||
|
||||
@@ -0,0 +1,70 @@
|
||||
"""
|
||||
==============================================
|
||||
Immunohistochemical staining colors separation
|
||||
==============================================
|
||||
|
||||
In this example we separate the immunohistochemical (IHC) staining
|
||||
from the hematoxylin counterstaining. The separation is achieved with the
|
||||
method described in [1]_, known as "color deconvolution".
|
||||
|
||||
The IHC staining expression of the FHL2 protein is here revealed with
|
||||
Diaminobenzidine (DAB) which gives a brown color.
|
||||
|
||||
|
||||
.. [1] 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.
|
||||
"""
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
from skimage import data
|
||||
from skimage.color import rgb2hed
|
||||
|
||||
ihc_rgb = data.immunohistochemistry()
|
||||
ihc_hed = rgb2hed(ihc_rgb)
|
||||
|
||||
fig, axes = plt.subplots(2, 2, figsize=(7, 6))
|
||||
ax0, ax1, ax2, ax3 = axes.ravel()
|
||||
|
||||
ax0.imshow(ihc_rgb)
|
||||
ax0.set_title("Original image")
|
||||
|
||||
ax1.imshow(ihc_hed[:, :, 0], cmap=plt.cm.gray)
|
||||
ax1.set_title("Hematoxylin")
|
||||
|
||||
ax2.imshow(ihc_hed[:, :, 1], cmap=plt.cm.gray)
|
||||
ax2.set_title("Eosin")
|
||||
|
||||
ax3.imshow(ihc_hed[:, :, 2], cmap=plt.cm.gray)
|
||||
ax3.set_title("DAB")
|
||||
|
||||
for ax in axes.ravel():
|
||||
ax.axis('off')
|
||||
|
||||
fig.subplots_adjust(hspace=0.3)
|
||||
|
||||
|
||||
"""
|
||||
.. image:: PLOT2RST.current_figure
|
||||
|
||||
Now we can easily manipulate the hematoxylin and DAB "channels":
|
||||
"""
|
||||
import numpy as np
|
||||
|
||||
from skimage.exposure import rescale_intensity
|
||||
|
||||
# Rescale hematoxylin and DAB signals and give them a fluorescence look
|
||||
h = rescale_intensity(ihc_hed[:, :, 0], out_range=(0, 1))
|
||||
d = rescale_intensity(ihc_hed[:, :, 2], out_range=(0, 1))
|
||||
zdh = np.dstack((np.zeros_like(h), d, h))
|
||||
|
||||
plt.figure()
|
||||
plt.imshow(zdh)
|
||||
plt.title("Stain separated image (rescaled)")
|
||||
plt.axis('off')
|
||||
plt.show()
|
||||
|
||||
"""
|
||||
.. image:: PLOT2RST.current_figure
|
||||
"""
|
||||
+278
-1
@@ -45,7 +45,14 @@ from __future__ import division
|
||||
|
||||
__all__ = ['convert_colorspace', 'rgb2hsv', 'hsv2rgb', 'rgb2xyz', 'xyz2rgb',
|
||||
'rgb2rgbcie', 'rgbcie2rgb', 'rgb2grey', 'rgb2gray', 'gray2rgb',
|
||||
'xyz2lab', 'lab2xyz', 'lab2rgb', 'rgb2lab', 'is_rgb', 'is_gray'
|
||||
'xyz2lab', 'lab2xyz', 'lab2rgb', 'rgb2lab', 'rgb2hed', 'hed2rgb',
|
||||
'separate_stains', 'combine_stains', 'rgb_from_hed', 'hed_from_rgb',
|
||||
'rgb_from_hdx', 'hdx_from_rgb', 'rgb_from_fgx', 'fgx_from_rgb',
|
||||
'rgb_from_bex', 'bex_from_rgb', 'rgb_from_rbd', 'rbd_from_rgb',
|
||||
'rgb_from_gdx', 'gdx_from_rgb', 'rgb_from_hax', 'hax_from_rgb',
|
||||
'rgb_from_bro', 'bro_from_rgb', 'rgb_from_bpx', 'bpx_from_rgb',
|
||||
'rgb_from_ahx', 'ahx_from_rgb', 'rgb_from_hpx', 'hpx_from_rgb',
|
||||
'is_rgb', 'is_gray'
|
||||
]
|
||||
|
||||
__docformat__ = "restructuredtext en"
|
||||
@@ -312,6 +319,90 @@ gray_from_rgb = np.array([[0.2125, 0.7154, 0.0721],
|
||||
# CIE LAB constants for Observer= 2A, Illuminant= D65
|
||||
lab_ref_white = np.array([0.95047, 1., 1.08883])
|
||||
|
||||
|
||||
# Haematoxylin-Eosin-DAB colorspace
|
||||
# From original Ruifrok's paper: 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.
|
||||
rgb_from_hed = np.array([[0.65, 0.70, 0.29],
|
||||
[0.07, 0.99, 0.11],
|
||||
[0.27, 0.57, 0.78]])
|
||||
hed_from_rgb = linalg.inv(rgb_from_hed)
|
||||
|
||||
# Following matrices are adapted form the Java code written by G.Landini.
|
||||
# The original code is available at:
|
||||
# http://www.dentistry.bham.ac.uk/landinig/software/cdeconv/cdeconv.html
|
||||
|
||||
# Hematoxylin + DAB
|
||||
rgb_from_hdx = np.array([[0.650, 0.704, 0.286],
|
||||
[0.268, 0.570, 0.776],
|
||||
[0.0, 0.0, 0.0]])
|
||||
rgb_from_hdx[2, :] = np.cross(rgb_from_hdx[0, :], rgb_from_hdx[1, :])
|
||||
hdx_from_rgb = linalg.inv(rgb_from_hdx)
|
||||
|
||||
# Feulgen + Light Green
|
||||
rgb_from_fgx = np.array([[0.46420921, 0.83008335, 0.30827187],
|
||||
[0.94705542, 0.25373821, 0.19650764],
|
||||
[0.0, 0.0, 0.0]])
|
||||
rgb_from_fgx[2, :] = np.cross(rgb_from_fgx[0, :], rgb_from_fgx[1, :])
|
||||
fgx_from_rgb = linalg.inv(rgb_from_fgx)
|
||||
|
||||
# Giemsa: Methyl Blue + Eosin
|
||||
rgb_from_bex = np.array([[0.834750233, 0.513556283, 0.196330403],
|
||||
[0.092789, 0.954111, 0.283111],
|
||||
[0.0, 0.0, 0.0]])
|
||||
rgb_from_bex[2, :] = np.cross(rgb_from_bex[0, :], rgb_from_bex[1, :])
|
||||
bex_from_rgb = linalg.inv(rgb_from_bex)
|
||||
|
||||
# FastRed + FastBlue + DAB
|
||||
rgb_from_rbd = np.array([[0.21393921, 0.85112669, 0.47794022],
|
||||
[0.74890292, 0.60624161, 0.26731082],
|
||||
[0.268, 0.570, 0.776]])
|
||||
rbd_from_rgb = linalg.inv(rgb_from_rbd)
|
||||
|
||||
# Methyl Green + DAB
|
||||
rgb_from_gdx = np.array([[0.98003, 0.144316, 0.133146],
|
||||
[0.268, 0.570, 0.776],
|
||||
[0.0, 0.0, 0.0]])
|
||||
rgb_from_gdx[2, :] = np.cross(rgb_from_gdx[0, :], rgb_from_gdx[1, :])
|
||||
gdx_from_rgb = linalg.inv(rgb_from_gdx)
|
||||
|
||||
# Hematoxylin + AEC
|
||||
rgb_from_hax = np.array([[0.650, 0.704, 0.286],
|
||||
[0.2743, 0.6796, 0.6803],
|
||||
[0.0, 0.0, 0.0]])
|
||||
rgb_from_hax[2, :] = np.cross(rgb_from_hax[0, :], rgb_from_hax[1, :])
|
||||
hax_from_rgb = linalg.inv(rgb_from_hax)
|
||||
|
||||
# Blue matrix Anilline Blue + Red matrix Azocarmine + Orange matrix Orange-G
|
||||
rgb_from_bro = np.array([[0.853033, 0.508733, 0.112656],
|
||||
[0.09289875, 0.8662008, 0.49098468],
|
||||
[0.10732849, 0.36765403, 0.9237484]])
|
||||
bro_from_rgb = linalg.inv(rgb_from_bro)
|
||||
|
||||
# Methyl Blue + Ponceau Fuchsin
|
||||
rgb_from_bpx = np.array([[0.7995107, 0.5913521, 0.10528667],
|
||||
[0.09997159, 0.73738605, 0.6680326],
|
||||
[0.0, 0.0, 0.0]])
|
||||
rgb_from_bpx[2, :] = np.cross(rgb_from_bpx[0, :], rgb_from_bpx[1, :])
|
||||
bpx_from_rgb = linalg.inv(rgb_from_bpx)
|
||||
|
||||
# Alcian Blue + Hematoxylin
|
||||
rgb_from_ahx = np.array([[0.874622, 0.457711, 0.158256],
|
||||
[0.552556, 0.7544, 0.353744],
|
||||
[0.0, 0.0, 0.0]])
|
||||
rgb_from_ahx[2, :] = np.cross(rgb_from_ahx[0, :], rgb_from_ahx[1, :])
|
||||
ahx_from_rgb = linalg.inv(rgb_from_ahx)
|
||||
|
||||
# Hematoxylin + PAS
|
||||
rgb_from_hpx = np.array([[0.644211, 0.716556, 0.266844],
|
||||
[0.175411, 0.972178, 0.154589],
|
||||
[0.0, 0.0, 0.0]])
|
||||
rgb_from_hpx[2, :] = np.cross(rgb_from_hpx[0, :], rgb_from_hpx[1, :])
|
||||
hpx_from_rgb = linalg.inv(rgb_from_hpx)
|
||||
|
||||
#-------------------------------------------------------------
|
||||
# The conversion functions that make use of the matrices above
|
||||
#-------------------------------------------------------------
|
||||
@@ -721,3 +812,189 @@ def lab2rgb(lab):
|
||||
This function uses lab2xyz and xyz2rgb.
|
||||
"""
|
||||
return xyz2rgb(lab2xyz(lab))
|
||||
|
||||
|
||||
def rgb2hed(rgb):
|
||||
"""RGB to Haematoxylin-Eosin-DAB (HED) color space conversion.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
rgb : array_like
|
||||
The image in RGB format, in a 3-D array of shape (.., .., 3).
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : ndarray
|
||||
The image in HED format, in a 3-D array of shape (.., .., 3).
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
If `rgb` is not a 3-D array of shape (.., .., 3).
|
||||
|
||||
|
||||
References
|
||||
----------
|
||||
.. [1] 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.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from skimage import data
|
||||
>>> from skimage.color import rgb2hed
|
||||
>>> ihc = data.immunohistochemistry()
|
||||
>>> ihc_hed = rgb2hed(ihc)
|
||||
"""
|
||||
return separate_stains(rgb, hed_from_rgb)
|
||||
|
||||
|
||||
def hed2rgb(hed):
|
||||
"""Haematoxylin-Eosin-DAB (HED) to RGB color space conversion.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
hed : array_like
|
||||
The image in the HED color space, in a 3-D array of shape (.., .., 3).
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : ndarray
|
||||
The image in RGB, in a 3-D array of shape (.., .., 3).
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
If `hed` is not a 3-D array of shape (.., .., 3).
|
||||
|
||||
References
|
||||
----------
|
||||
.. [1] 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.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from skimage import data
|
||||
>>> from skimage.color import rgb2hed, hed2rgb
|
||||
>>> ihc = data.immunohistochemistry()
|
||||
>>> ihc_hed = rgb2hed(ihc)
|
||||
>>> ihc_rgb = hed2rgb(ihc_hed)
|
||||
"""
|
||||
return combine_stains(hed, rgb_from_hed)
|
||||
|
||||
|
||||
def separate_stains(rgb, conv_matrix):
|
||||
"""RGB to stain color space conversion.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
rgb : array_like
|
||||
The image in RGB format, in a 3-D array of shape (.., .., 3).
|
||||
conv_matrix: ndarray
|
||||
The stain separation matrix as described by G. Landini [1]_.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : ndarray
|
||||
The image in stain color space, in a 3-D array of shape (.., .., 3).
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
If `rgb` is not a 3-D array of shape (.., .., 3).
|
||||
|
||||
Notes
|
||||
-----
|
||||
Stain separation matrices available in the ``color`` module and their
|
||||
respective colorspace:
|
||||
|
||||
* ``hed_from_rgb``: Hematoxylin + Eosin + DAB
|
||||
* ``hdx_from_rgb``: Hematoxylin + DAB
|
||||
* ``fgx_from_rgb``: Feulgen + Light Green
|
||||
* ``bex_from_rgb``: Giemsa stain : Methyl Blue + Eosin
|
||||
* ``rbd_from_rgb``: FastRed + FastBlue + DAB
|
||||
* ``gdx_from_rgb``: Methyl Green + DAB
|
||||
* ``hax_from_rgb``: Hematoxylin + AEC
|
||||
* ``bro_from_rgb``: Blue matrix Anilline Blue + Red matrix Azocarmine\
|
||||
+ Orange matrix Orange-G
|
||||
* ``bpx_from_rgb``: Methyl Blue + Ponceau Fuchsin
|
||||
* ``ahx_from_rgb``: Alcian Blue + Hematoxylin
|
||||
* ``hpx_from_rgb``: Hematoxylin + PAS
|
||||
|
||||
References
|
||||
----------
|
||||
.. [1] http://www.dentistry.bham.ac.uk/landinig/software/cdeconv/cdeconv.html
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from skimage import data
|
||||
>>> from skimage.color import separate_stains, hdx_from_rgb
|
||||
>>> ihc = data.immunohistochemistry()
|
||||
>>> ihc_hdx = separate_stains(ihc, hdx_from_rgb)
|
||||
"""
|
||||
rgb = dtype.img_as_float(rgb) + 2
|
||||
stains = np.dot(np.reshape(-np.log(rgb), (-1, 3)), conv_matrix)
|
||||
return np.reshape(stains, rgb.shape)
|
||||
|
||||
|
||||
def combine_stains(stains, conv_matrix):
|
||||
"""Stain to RGB color space conversion.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
stains : array_like
|
||||
The image in stain color space, in a 3-D array of shape (.., .., 3).
|
||||
conv_matrix: ndarray
|
||||
The stain separation matrix as described by G. Landini [1]_.
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : ndarray
|
||||
The image in RGB format, in a 3-D array of shape (.., .., 3).
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
If `stains` is not a 3-D array of shape (.., .., 3).
|
||||
|
||||
Notes
|
||||
-----
|
||||
Stain combination matrices available in the ``color`` module and their
|
||||
respective colorspace:
|
||||
|
||||
* ``rgb_from_hed``: Hematoxylin + Eosin + DAB
|
||||
* ``rgb_from_hdx``: Hematoxylin + DAB
|
||||
* ``rgb_from_fgx``: Feulgen + Light Green
|
||||
* ``rgb_from_bex``: Giemsa stain : Methyl Blue + Eosin
|
||||
* ``rgb_from_rbd``: FastRed + FastBlue + DAB
|
||||
* ``rgb_from_gdx``: Methyl Green + DAB
|
||||
* ``rgb_from_hax``: Hematoxylin + AEC
|
||||
* ``rgb_from_bro``: Blue matrix Anilline Blue + Red matrix Azocarmine\
|
||||
+ Orange matrix Orange-G
|
||||
* ``rgb_from_bpx``: Methyl Blue + Ponceau Fuchsin
|
||||
* ``rgb_from_ahx``: Alcian Blue + Hematoxylin
|
||||
* ``rgb_from_hpx``: Hematoxylin + PAS
|
||||
|
||||
References
|
||||
----------
|
||||
.. [1] http://www.dentistry.bham.ac.uk/landinig/software/cdeconv/cdeconv.html
|
||||
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from skimage import data
|
||||
>>> from skimage.color import (separate_stains, combine_stains,
|
||||
... hdx_from_rgb, rgb_from_hdx)
|
||||
>>> ihc = data.immunohistochemistry()
|
||||
>>> ihc_hdx = separate_stains(ihc, hdx_from_rgb)
|
||||
>>> ihc_rgb = combine_stains(ihc_hdx, rgb_from_hdx)
|
||||
"""
|
||||
from ..exposure import rescale_intensity
|
||||
|
||||
stains = dtype.img_as_float(stains)
|
||||
logrgb2 = np.dot(-np.reshape(stains, (-1, 3)), conv_matrix)
|
||||
rgb2 = np.exp(logrgb2)
|
||||
return rescale_intensity(np.reshape(rgb2 - 2, stains.shape), in_range=(-1, 1))
|
||||
|
||||
@@ -16,11 +16,14 @@ import os.path
|
||||
import numpy as np
|
||||
from numpy.testing import *
|
||||
|
||||
from skimage import img_as_float
|
||||
from skimage import img_as_float, img_as_ubyte
|
||||
from skimage.io import imread
|
||||
from skimage.color import (
|
||||
rgb2hsv, hsv2rgb,
|
||||
rgb2xyz, xyz2rgb,
|
||||
rgb2hed, hed2rgb,
|
||||
separate_stains,
|
||||
combine_stains,
|
||||
rgb2rgbcie, rgbcie2rgb,
|
||||
convert_colorspace,
|
||||
rgb2grey, gray2rgb,
|
||||
@@ -121,6 +124,32 @@ class TestColorconv(TestCase):
|
||||
img_rgb = img_as_float(self.img_rgb)
|
||||
assert_array_almost_equal(xyz2rgb(rgb2xyz(img_rgb)), img_rgb)
|
||||
|
||||
# RGB<->HED roundtrip with ubyte image
|
||||
def test_hed_rgb_roundtrip(self):
|
||||
img_rgb = self.img_rgb
|
||||
assert_equal(img_as_ubyte(hed2rgb(rgb2hed(img_rgb))), img_rgb)
|
||||
|
||||
# RGB<->HED roundtrip with float image
|
||||
def test_hed_rgb_float_roundtrip(self):
|
||||
img_rgb = img_as_float(self.img_rgb)
|
||||
assert_array_almost_equal(hed2rgb(rgb2hed(img_rgb)), img_rgb)
|
||||
|
||||
# RGB<->HDX roundtrip with ubyte image
|
||||
def test_hdx_rgb_roundtrip(self):
|
||||
from skimage.color.colorconv import hdx_from_rgb, rgb_from_hdx
|
||||
img_rgb = self.img_rgb
|
||||
conv = combine_stains(separate_stains(img_rgb, hdx_from_rgb),
|
||||
rgb_from_hdx)
|
||||
assert_equal(img_as_ubyte(conv), img_rgb)
|
||||
|
||||
# RGB<->HDX roundtrip with ubyte image
|
||||
def test_hdx_rgb_roundtrip(self):
|
||||
from skimage.color.colorconv import hdx_from_rgb, rgb_from_hdx
|
||||
img_rgb = img_as_float(self.img_rgb)
|
||||
conv = combine_stains(separate_stains(img_rgb, hdx_from_rgb),
|
||||
rgb_from_hdx)
|
||||
assert_array_almost_equal(conv, img_rgb)
|
||||
|
||||
# RGB to RGB CIE
|
||||
def test_rgb2rgbcie_conversion(self):
|
||||
gt = np.array([[[ 0.1488856 , 0.18288098, 0.19277574],
|
||||
|
||||
@@ -127,3 +127,19 @@ def clock():
|
||||
|
||||
"""
|
||||
return load("clock_motion.png")
|
||||
|
||||
|
||||
def immunohistochemistry():
|
||||
"""Immunohistochemical (IHC) staining with hematoxylin counterstaining.
|
||||
|
||||
This picture shows colonic glands where the IHC expression of FHL2 protein
|
||||
is revealed with DAB. Hematoxylin counterstaining is applied to enhance the
|
||||
negative parts of the tissue.
|
||||
|
||||
This image was acquired at the Center for Microscopy And Molecular Imaging
|
||||
(CMMI).
|
||||
|
||||
No known copyright restrictions.
|
||||
|
||||
"""
|
||||
return load("ihc.jpg")
|
||||
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 226 KiB |
Reference in New Issue
Block a user