Rolling ball and sliding paraboloid background subtraction algorithms

Fully Ported to Python from ImageJ's Background Subtractor. Only works for 8-bit greyscale images currently. Based on the concept of the rolling ball algorithm described in Stanley Sternberg's article, "Biomedical Image Processing", IEEE Computer, January 1983. Imagine that the 2D grayscale image has a third (height) dimension by the image value at every point in the image, creating a surface. A ball of given radius is rolled over the bottom side of this surface; the hull of the volume reachable by the ball is the background. http://rsbweb.nih.gov/ij/developer/source/ij/plugin/filter/BackgroundSubtracter.java.html

This algorithms are perfect for microscope images, to distinguish particles from background.

Installation

pip install opencv-rolling-ball

Usage

import cv2
from cv2_rolling_ball import subtract_background_rolling_ball

img = cv2.imread(f'path/to/img.tif', 0)
img = subtract_background_rolling_ball(img, 30, light_background=True,
                                     use_paraboloid=False, do_presmooth=True,
                                     create_background=False)
background = subtract_background_rolling_ball(img, 30, light_background=True,
                                     use_paraboloid=False, do_presmooth=True,
                                     create_background=True)

Example outputs

Input

Subtracted background

Without background