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synced 2026-07-03 06:19:39 +08:00
FIX: fixed docstrings. ENH: Added new stain separation matrices.
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spotter
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@@ -325,6 +325,78 @@ rgb_from_hed = np.array([[0.65, 0.70, 0.29],
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[0.27, 0.57, 0.78]])
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hed_from_rgb = linalg.inv(rgb_from_hed)
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# Following matrices are adapted form the Java code written by G.Landini.
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# The original code is available at:
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# http://www.dentistry.bham.ac.uk/landinig/software/cdeconv/cdeconv.html
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# Hematoxylin + DAB
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rgb_from_hdx = np.array([[0.650, 0.704, 0.286],
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[0.268, 0.570, 0.776],
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[0.0, 0.0, 0.0]])
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rgb_from_hdx[2, :] = np.cross(rgb_from_hdx[0, :], rgb_from_hdx[1, :])
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hdx_from_rgb = linalg.inv(rgb_from_hdx)
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# Feulgen + Light Green
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rgb_from_fgx = np.array([[0.46420921, 0.83008335, 0.30827187],
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[0.94705542, 0.25373821, 0.19650764],
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[0.0, 0.0, 0.0]])
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rgb_from_fgx[2, :] = np.cross(rgb_from_fgx[0, :], rgb_from_fgx[1, :])
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fgx_from_rgb = linalg.inv(rgb_from_fgx)
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# Giemsa: Methyl Blue + Eosin
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rgb_from_bex = np.array([[0.834750233, 0.513556283, 0.196330403],
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[0.092789, 0.954111, 0.283111],
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[0.0, 0.0, 0.0]])
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rgb_from_bex[2, :] = np.cross(rgb_from_bex[0, :], rgb_from_bex[1, :])
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bex_from_rgb = linalg.inv(rgb_from_bex)
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# FastRed + FastBlue + DAB
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rgb_from_rbd = np.array([[0.21393921, 0.85112669, 0.47794022],
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[0.74890292, 0.60624161, 0.26731082],
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[0.268, 0.570, 0.776]])
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rbd_from_rgb = linalg.inv(rgb_from_rbd)
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# Methyl Green + DAB
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rgb_from_gdx = np.array([[0.98003, 0.144316, 0.133146],
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[0.268, 0.570, 0.776],
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[0.0, 0.0, 0.0]])
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rgb_from_gdx[2, :] = np.cross(rgb_from_gdx[0, :], rgb_from_gdx[1, :])
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gdx_from_rgb = linalg.inv(rgb_from_gdx)
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# Hematoxylin + AEC
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rgb_from_hax = np.array([[0.650, 0.704, 0.286],
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[0.2743, 0.6796, 0.6803],
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[0.0, 0.0, 0.0]])
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rgb_from_hax[2, :] = np.cross(rgb_from_hax[0, :], rgb_from_hax[1, :])
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hax_from_rgb = linalg.inv(rgb_from_hax)
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# Blue matrix Anilline Blue + Red matrix Azocarmine + Orange matrix Orange-G
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rgb_from_bro = np.array([[0.853033, 0.508733, 0.112656],
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[0.09289875, 0.8662008, 0.49098468],
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[0.10732849, 0.36765403, 0.9237484]])
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bro_from_rgb = linalg.inv(rgb_from_bro)
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# Methyl Blue + Ponceau Fuchsin
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rgb_from_bpx = np.array([[0.7995107, 0.5913521, 0.10528667],
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[0.09997159, 0.73738605, 0.6680326],
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[0.0, 0.0, 0.0]])
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rgb_from_bpx[2, :] = np.cross(rgb_from_bpx[0, :], rgb_from_bpx[1, :])
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bpx_from_rgb = linalg.inv(rgb_from_bpx)
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# Alcian Blue + Hematoxylin
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rgb_from_ahx = np.array([[0.874622, 0.457711, 0.158256],
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[0.552556, 0.7544, 0.353744],
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[0.0, 0.0, 0.0]])
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rgb_from_ahx[2, :] = np.cross(rgb_from_ahx[0, :], rgb_from_ahx[1, :])
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ahx_from_rgb = linalg.inv(rgb_from_ahx)
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# Hematoxylin + PAS
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rgb_from_hpx = np.array([[0.644211, 0.716556, 0.266844],
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[0.175411, 0.972178, 0.154589],
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[0.0, 0.0, 0.0]])
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rgb_from_hpx[2, :] = np.cross(rgb_from_hpx[0, :], rgb_from_hpx[1, :])
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hpx_from_rgb = linalg.inv(rgb_from_hpx)
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#-------------------------------------------------------------
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# The conversion functions that make use of the matrices above
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#-------------------------------------------------------------
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@@ -766,7 +838,7 @@ def rgb2hed(rgb):
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--------
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>>> from skimage import data
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>>> from skimage.color import rgb2hed
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>>> ihc = data.ihc()
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>>> ihc = data.immunohistochemistry()
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>>> ihc_hed = rgb2hed(ihc)
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"""
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rgb = dtype.img_as_float(rgb) + 2
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@@ -780,12 +852,12 @@ def hed2rgb(hed):
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Parameters
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----------
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hed : array_like
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The image in HED format, in a 3-D array of shape (.., .., 3).
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The image in the HED color space, in a 3-D array of shape (.., .., 3).
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Returns
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-------
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out : ndarray
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The image in HED format, in a 3-D array of shape (.., .., 3).
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The image in RGB, in a 3-D array of shape (.., .., 3).
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Raises
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------
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@@ -803,7 +875,7 @@ def hed2rgb(hed):
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--------
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>>> from skimage import data
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>>> from skimage.color import rgb2hed, hed2rgb
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>>> ihc = data.ihc()
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>>> ihc = data.immunohistochemistry()
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>>> ihc_hed = rgb2hed(ihc)
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>>> ihc_rgb = hed2rgb(ihc_hed)
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"""
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@@ -813,3 +885,83 @@ def hed2rgb(hed):
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logrgb1 = np.dot(-np.reshape(hed, (-1, 3)), rgb_from_hed)
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rgb1 = np.exp(logrgb1)
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return rescale_intensity(np.reshape(rgb1 - 2, hed.shape), in_range=(-1, 1))
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def separate_stains(rgb, conv_matrix):
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"""RGB to stain color space conversion.
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Parameters
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----------
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rgb : array_like
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The image in RGB format, in a 3-D array of shape (.., .., 3).
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conv_matrix: ndarray
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The stain separation matrix as described by G. Landini [1]_.
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Returns
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-------
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out : ndarray
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The image in stain color space, in a 3-D array of shape (.., .., 3).
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Raises
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------
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ValueError
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If `rgb` is not a 3-D array of shape (.., .., 3).
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References
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----------
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.. [1] http://www.dentistry.bham.ac.uk/landinig/software/cdeconv/cdeconv.html
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Examples
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--------
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>>> from skimage import data
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>>> from skimage.color import separate_stains
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>>> from skimage.color.colorconv import hdx_from_rgb
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>>> ihc = data.immunohistochemistry()
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>>> ihc_hdx = separate_stains(ihc, hdx_from_rgb)
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"""
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rgb = dtype.img_as_float(rgb) + 2
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stains = np.dot(np.reshape(-np.log(rgb), (-1, 3)), conv_matrix)
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return np.reshape(stains, rgb.shape)
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def combine_stains(stains, conv_matrix):
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"""Haematoxylin-Eosin-DAB (HED) to RGB color space conversion.
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Parameters
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----------
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stains : array_like
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The image in stain color space, in a 3-D array of shape (.., .., 3).
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conv_matrix: ndarray
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The stain separation matrix as described by G. Landini [1]_.
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Returns
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-------
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out : ndarray
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The image in RGB format, in a 3-D array of shape (.., .., 3).
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Raises
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------
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ValueError
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If `stains` is not a 3-D array of shape (.., .., 3).
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References
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----------
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.. [1] http://www.dentistry.bham.ac.uk/landinig/software/cdeconv/cdeconv.html
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Examples
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--------
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>>> from skimage import data
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>>> from skimage.color import separate_stains, combine_stains
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>>> from skimage.color.colorconv import hdx_from_rgb, rgb_from_hdx
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>>> ihc = data.immunohistochemistry()
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>>> ihc_hdx = separate_stains(ihc, hdx_from_rgb)
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>>> ihc_rgb = combine_stains(ihc_hdx, rgb_from_hdx)
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"""
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from ..exposure import rescale_intensity
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stains = dtype.img_as_float(stains)
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logrgb2 = np.dot(-np.reshape(stains, (-1, 3)), conv_matrix)
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rgb2 = np.exp(logrgb2)
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return rescale_intensity(np.reshape(rgb2 - 2, stains.shape), in_range=(-1, 1))
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