Clipping Percentile To Zero

How to use ClipPercentileToZero to clip high and low intensity values of image planes or image volumes in an ImageStack.

ClipPercentileToZero is the recommended FilterAlgorithm for clipping images for most users. An interval is defined by pixel values at the p_min and p_max percentiles of the distribution. The interval bounds can also be scaled by multiplying with a min_coeff and max_coeff. Any pixel values that fall outside the interval are clipped to the interval edges. Lastly, the pixel values are shifted such that the minimum value is set to zero. See Clip if you don’t want values to be shifted to zero.

The minimum percentile p_min is useful for removing low-intensity background by setting everything below p_min to zero. The maximum percentile p_max is useful for eliminating high-intensity outliers. If your ImageStack has greater than one z-plane, it is critical to set is_volume=True to get the expected clipping behavior.

To see how ClipPercentileToZero can be used for normalizing images see Normalizing Intensity Values.

# Load ImageStack from example BaristaSeq data
import matplotlib.pyplot as plt
from starfish.types import Axes, Levels
from starfish import FieldOfView
from starfish.image import Filter
from starfish.util.plot import imshow_plane, intensity_histogram

bs_experiment =
stack = bs_experiment.fov().get_image(FieldOfView.PRIMARY_IMAGES)

# Define some useful functions for viewing multiple images and histograms
def plot_intensity_histograms(stack: starfish.ImageStack, r: int, title: str):
    fig = plt.figure(dpi=150)
    ax1 = fig.add_subplot(131, title='ch: 0')
    ax2 = fig.add_subplot(132, title='ch: 1', sharex=ax1, sharey=ax1)
    ax3 = fig.add_subplot(133, title='ch: 2', sharex=ax1, sharey=ax1)
    intensity_histogram(stack, sel={Axes.ROUND: r, Axes.CH: 0}, log=True, bins=50, ax=ax1)
    intensity_histogram(stack, sel={Axes.ROUND: r, Axes.CH: 1}, log=True, bins=50, ax=ax2)
    intensity_histogram(stack, sel={Axes.ROUND: r, Axes.CH: 2}, log=True, bins=50, ax=ax3)

# View distribution of intensities in round 1
plot_intensity_histograms(stack=stack, r=1, title='Distribution before clipping')

# Clip imagestack without setting is_volume to True
bad_clipper = Filter.ClipPercentileToZero(p_min=90, p_max=99.99, level_method=Levels.SCALE_BY_CHUNK)
bad_stack =

# View distribution if you forget to set is_volume
plot_intensity_histograms(bad_stack, r=1, title='Distribution after clipping with is_volume=False')

# Clip imagestack without setting is_volume to True
clipper = Filter.ClipPercentileToZero(p_min=90, p_max=99.99, is_volume=True, level_method=Levels.SCALE_BY_CHUNK), in_place=True)

# View distribution
plot_intensity_histograms(stack, r=1, title='Distribution after clipping with is_volume=True')

Gallery generated by Sphinx-Gallery