diff --git a/docs/tutorials/image.rst b/docs/tutorials/image.rst
index 910146be..387fc73d 100644
--- a/docs/tutorials/image.rst
+++ b/docs/tutorials/image.rst
@@ -27,7 +27,43 @@ Diffraction patterns can come in a variety of exotic file formats. Scikit-ued ha
 * All other file formats supported by `scikit-image`_.
 
 The :func:`diffread` function will transparently distinguish between those formats and dispatch to the right functions. 
+.. _automatic_mask_generation:
 
+Automatic generation of a mask as needed for the autocenter() function
+======================================================================
+
+Automatic mask generation
+-------------------------
+A mask can be used for the :func: `autocenter` function to exclude the beam stopper from the analysis and only keep valuable 
+information. Such a mask can automatically be generated using this function; given a diffraction pattern, the mask will be created to block 
+the darkest regions of the image (default blocks about 10% of the darker values).
+Here is an example: 
+
+.. plot::
+
+	import matplotlib.pyplot as plt
+	from skued import diffread, auto_masking
+
+	image = diffread('data/Cr_1.tif')
+	mask = auto_masking(image, threshold = 0.1)
+
+	# Reduce size of images because of memory usage of ReadTheDocs
+	image = image[::3, ::3]
+	mask = mask[::3, ::3]
+
+	fig , (ax1, ax2) = plt.subplots(1,2, figsize = (9,3))
+	ax1.imshow(image, vmin=0, vmax=150, cmap='inferno')
+	ax2.imshow(mask, vmin=0, vmax=1, cmap='inferno')
+
+	for ax in (ax1, ax2):
+		ax.xaxis.set_visible(False)
+		ax.yaxis.set_visible(False)
+
+	ax1.set_title('Cr_1')
+	ax2.set_title('Mask')
+
+	plt.tight_layout()
+	plt.show()
 .. _alignment:
 
 Automatic center-finding
diff --git a/skued/__init__.py b/skued/__init__.py
index ce5e55af..fdcc97b5 100644
--- a/skued/__init__.py
+++ b/skued/__init__.py
@@ -44,6 +44,7 @@
 from .image import (
     align,
     autocenter,
+    auto_masking,
     azimuthal_average,
     brillouin_zones,
     combine_masks,
diff --git a/skued/image/__init__.py b/skued/image/__init__.py
index 0c57b242..3443bf15 100644
--- a/skued/image/__init__.py
+++ b/skued/image/__init__.py
@@ -4,7 +4,7 @@
 from .alignment import align, ialign, itrack_peak
 from .brillouin import brillouin_zones
 from .calibration import detector_scattvectors, powder_calq
-from .center import autocenter
+from .center import autocenter, auto_masking
 from .indexing import bragg_peaks, bragg_peaks_persistence
 from .metrics import (
     combine_masks,
diff --git a/skued/image/center.py b/skued/image/center.py
index 106a2f5b..0db83606 100644
--- a/skued/image/center.py
+++ b/skued/image/center.py
@@ -124,3 +124,29 @@ def _center_of_intensity(im, mask=None):
     r_ = np.average(rr, weights=weights)
     c_ = np.average(cc, weights=weights)
     return int(r_), int(c_)
+
+
+def auto_masking(im, threshold=0.1):
+    """
+    Generate a mask based on the darkest fraction of an image
+
+    Parameters
+    ----------
+    im : floats, ndarrays of shape (N,M)
+        image used to generate a mask
+    threshold: float, optional
+        fraction of the lowest values to be masked, default = 15%
+
+    Yields
+    ------
+    mask : boolean, ndarrays of shape (N,M)
+        Mask that evaluates to True on valid pixels.
+
+    """
+    # Find the median of the highest intensity value of the image to avoid hot spots
+    max_median = np.median([max(x) for x in np.real(im)])
+    # Set the threshold value
+    lower_limit = threshold * max_median
+    # generate a mask
+    mask = im >= lower_limit
+    return mask