def reinhard_stats(slide_path, sample_fraction, magnification=None):
"""Samples a whole-slide-image to determine colorspace statistics (mean,
variance) needed to perform global Reinhard color normalization.
Normalizing individual tiles independently creates a significant bias
in the results of segmentation and feature extraction, as the color
statistics of each tile in a whole-slide image can vary significantly.
To remedy this, we sample a subset of pixels from the entire whole-slide
image in order to estimate the global mean and standard deviation of
each channel in the Lab color space that are needed for reinhard color
normalization.
Parameters
----------
slide_path : str
path and filename of slide.
sample_fraction : double
Fraction of pixels to sample (range (0, 1]).
magnification : scalar
Desired magnification for sampling. Defaults to native scan
magnification.
Returns
-------
Mu : array_like
A 3-element array containing the means of the target image channels
in sample_pix_lab color space.
Sigma : array_like
A 3-element list containing the standard deviations of the target image
channels in sample_pix_lab color space.
Notes
-----
Returns a namedtuple.
See Also
--------
histomicstk.preprocessing.color_conversion.lab_mean_std
histomicstk.preprocessing.color_normalization.reinhard
"""
# generate a sampling of sample_pixels_rgb pixels from whole-slide image
sample_pixels_rgb = sample_pixels(slide_path, sample_fraction,
magnification)
# reshape the Nx3 pixel array into a 1 x N x 3 image for lab_mean_std
sample_pixels_rgb = np.reshape(sample_pixels_rgb,
(1, sample_pixels_rgb.shape[0], 3))
# compute mean and stddev of sample pixels in Lab space
Mu, Sigma = color_conversion.lab_mean_std(sample_pixels_rgb)
# build named tuple for output
ReinhardStats = collections.namedtuple('ReinhardStats', ['Mu', 'Sigma'])
stats = ReinhardStats(Mu, Sigma)
return stats
def sample_pixels(args):
"""Version of histomicstk.utils.sample_pixels that takes a Namespace
and handles the special default values.
"""
args = (args._asdict() if hasattr(args, '_asdict') else vars(args)).copy()
for k in 'magnification', 'sample_fraction', 'sample_approximate_total':
if args[k] == -1:
del args[k]
return htk_utils.sample_pixels(**args)
def reinhard_stats(slide_path, magnification, sample_percent):
"""Samples a whole-slide-image to determine colorspace statistics (mean,
variance) needed to perform global Reinhard color normalization.
Normalizing individual tiles independently creates a significant bias
in the results of segmentation and feature extraction, as the color
statistics of each tile in a whole-slide image can vary significantly.
To remedy this, we sample a subset of pixels from the entire whole-slide
image in order to estimate the global mean and standard deviation of
each channel in the Lab color space that are needed for reinhard color
normalization.
Parameters
----------
slide_path : str
path and filename of slide.
magnification : scalar
Desired magnification for sampling. Defaults to native scan
magnification.
sample_percent : double
Percentage of pixels to sample (range (0, 1]).
Returns
-------
Mu : array_like
A 3-element array containing the means of the target image channels
in sample_pix_lab color space.
Sigma : array_like
A 3-element list containing the standard deviations of the target image
channels in sample_pix_lab color space.
Notes
-----
Returns a namedtuple.
See Also
--------
histomicstk.preprocessing.color_conversion.lab_mean_std
histomicstk.preprocessing.color_normalization.reinhard
"""
# generate a sampling of sample_pixels_rgb pixels from whole-slide image
sample_pixels_rgb = sample_pixels(slide_path, magnification,
sample_percent)
# reshape the Nx3 pixel array into a 1 x N x 3 image for lab_mean_std
sample_pixels_rgb = np.reshape(sample_pixels_rgb,
(1, sample_pixels_rgb.shape[0], 3))
# compute mean and stddev of sample pixels in Lab space
Mu, Sigma = color_conversion.lab_mean_std(sample_pixels_rgb)
# build named tuple for output
ReinhardStats = collections.namedtuple('ReinhardStats', ['Mu', 'Sigma'])
stats = ReinhardStats(Mu, Sigma)
return stats