Minor enhancements

doc/examples/filters/plot_threshold_minimum.py

@@ -7,7 +7,7 @@ The minimum algorithm takes a histogram of the image and smooths it
 repeatedly until there are only two peaks in the histogram.  Then it
 finds the minimum value between the two peaks.  After smoothing the
 histogram, there can be multiple pixel values with the minimum histogram
-count, so you can pick 'min', 'mid', or 'max' of these values.
+count, so you can pick the 'min', 'mid', or 'max' of these values.
 
 """
 import matplotlib.pyplot as plt

skimage/filters/thresholding.py

@@ -112,7 +112,7 @@ def threshold_otsu(image, nbins=256):
     threshold : float
         Upper threshold value. All pixels intensities that less or equal of
         this value assumed as foreground.
-        
+
     Raises
     ------
     ValueError
@@ -395,11 +395,11 @@ def threshold_li(image):
     return threshold + immin
 
 
-def threshold_minimum(image, nbins=256, bias='min'):
+def threshold_minimum(image, nbins=256, bias='min', max_iter=10000):
     """Return threshold value based on minimum method.
 
-    A histogram is computed and smoothed until there are only two maximums.
-    Then the minimum between these is found.
+    The histogram of the input `image` is computed and smoothed until there are
+    only two maxima. Then the minimum in between is the threshold value.
 
     Parameters
     ----------
@@ -411,11 +411,14 @@ def threshold_minimum(image, nbins=256, bias='min'):
     bias : {'min', 'mid', 'max'}, optional
         'min', 'mid', 'max' return lowest, middle, or highest pixel value
         with minimum histogram value.
+    max_iter: int, optional
+        Maximum number of iterations to smooth the histogram.
 
     Returns
     -------
     threshold : float
-        Computed threshold value.
+        Upper threshold value. All pixels with an intensity higher than
+        this value are assumed to be foreground.
 
     Raises
     ------
@@ -456,7 +459,6 @@ def threshold_minimum(image, nbins=256, bias='min'):
 
     hist, bin_centers = histogram(image.ravel(), nbins)
 
-    max_iter = 10000
     smooth_hist = np.copy(hist)
     for counter in range(max_iter):
         smooth_hist = ndif.uniform_filter1d(smooth_hist, 3)
@@ -466,7 +468,7 @@ def threshold_minimum(image, nbins=256, bias='min'):
     if len(maximums) != 2:
         raise RuntimeError('Unable to find two maxima in histogram')
     elif counter == max_iter - 1:
-        raise RuntimeError('Maximum iteration reached for histogram histogram'
+        raise RuntimeError('Maximum iteration reached for histogram'
                            'smoothing')
 
     # Find lowest point between the maxima, biased to the low end (min)