def get_background_threshold(image, mask = None):
    """Get threshold based on the mode of the image
    The threshold is calculated by calculating the mode and multiplying by
    2 (an arbitrary empirical factor). The user will presumably adjust the
    multiplication factor as needed."""
    cropped_image = np.array(image.flat) if mask is None else image[mask]
    if np.product(cropped_image.shape)==0:
        return 0
    img_min = np.min(cropped_image)
    img_max = np.max(cropped_image)
    if img_min == img_max:
        return cropped_image[0]
    
    # Only do the histogram between values a bit removed from saturation
    robust_min = 0.02 * (img_max - img_min) + img_min
    robust_max = 0.98 * (img_max - img_min) + img_min
    nbins = 256
    cropped_image = cropped_image[np.logical_and(cropped_image > robust_min,
                                                 cropped_image < robust_max)]
    if len(cropped_image) == 0:
        return robust_min
    
    h = scipy.ndimage.histogram(cropped_image, robust_min, robust_max, nbins)
    index = np.argmax(h)
    cutoff = float(index) / float(nbins-1)
    #
    # If we have a low (or almost no) background, the cutoff will be
    # zero since the background falls into the lowest bin. We want to
    # offset by the robust cutoff factor of .02. We rescale by 1.04
    # to account for the 0.02 at the top and bottom.
    #
    cutoff = (cutoff + 0.02) / 1.04
    return img_min + cutoff * 2 * (img_max - img_min)
Get threshold based on the mode of the image
    The threshold is calculated by calculating the mode and multiplying by
    2 (an arbitrary empirical factor). The user will presumably adjust the
    multiplication factor as needed.