Thresholding is used to create a binary image from a grayscale image [1].


See also

A more comprehensive presentation on Thresholding

We illustrate how to apply one of these thresholding algorithms. Otsu’s method [2] calculates an “optimal” threshold (marked by a red line in the histogram below) by maximizing the variance between two classes of pixels, which are separated by the threshold. Equivalently, this threshold minimizes the intra-class variance.

import matplotlib.pyplot as plt
from skimage import data
from skimage.filters import threshold_otsu

image =
thresh = threshold_otsu(image)
binary = image > thresh

fig, axes = plt.subplots(ncols=3, figsize=(8, 2.5))
ax = axes.ravel()
ax[0] = plt.subplot(1, 3, 1, adjustable='box-forced')
ax[1] = plt.subplot(1, 3, 2)
ax[2] = plt.subplot(1, 3, 3, sharex=ax[0], sharey=ax[0], adjustable='box-forced')


ax[1].hist(image.ravel(), bins=256)
ax[1].axvline(thresh, color='r')


If you are not familiar with the details of the different algorithms and the underlying assumptions, it is often difficult to know which algorithm will give the best results. Therefore, Scikit-image includes a function to evaluate thresholding algorithms provided by the library. At a glance, you can select the best algorithm for you data without a deep understanding of their mechanisms.

from skimage.filters import try_all_threshold

img =

# Here, we specify a radius for local thresholding algorithms.
# If it is not specified, only global algorithms are called.
fig, ax = try_all_threshold(img, figsize=(10, 8), verbose=False)

Total running time of the script: ( 0 minutes 1.470 seconds)

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