The following code block shows how the remove_small_objects() function can be used to remove objects smaller than a specified minimum size threshold—the higher the specified threshold, the more objects get removed:

from skimage.morphology import remove_small_objects
im = rgb2gray(imread('../images/circles.jpg'))
im[im > 0.5] = 1 # create binary image by thresholding with fixed threshold
0.5
im[im <= 0.5] = 0
im = im.astype(np.bool)
pylab.figure(figsize=(20,20))
pylab.subplot(2,2,1), plot_image(im, 'original')
i = 2
for osz in [50, 200, 500]:
    im1 = remove_small_objects(im, osz, connectivity=1)
    pylab.subplot(2,2,i), plot_image(im1, 'removing small objects below size ' + str(osz))
    i += 1
pylab.show()

The following screenshot shows the output of the previous code block. As expected, the higher the minimum size threshold specified, the more objects are removed: