Prerequisites

Before starting this lesson, you should be familiar with:

• Binarization

• Lookup tables

• Data types

Learning Objectives

After completing this lesson, learners should be able to:

• Understand how objects in images are represented as a label mask image.

• Apply connected component labeling to a binary image to create a label mask image.

Motivation

A main task of bioimage analysis is to detect objects in images. To do so one needs to be able to label pixels that are part of the same object in a way that this can be efficiently stored and processed by the computer. A prevalent way of doing this is connected component labeling, which is discussed in this module.

Concept map

Figure

Activities

1. 2D connected component labeling:
   • Open image xy_8bit_binary__nuclei.tif.
   • Create a label image by means of 4-connected connected components labeling.
   • Apply a multi-color LUT.
   • Repeat with 8-connected labeling and discuss the difference.
2. 3D connected component labeling:
   • Open image xyz_8bit_binary__spots.tif
   • Inspect the pixel values of the label image and see what you can learn about the objects that are encoded in this image.

ImageJ MorpholibJ Macro & GUI

/**
 * 2D and 3D connected components labeling
 * 
 * Required update sites: 
 *   - IJPB-Plugins (MorpholibJ)
 */

run("Close All");

//  2D connected components labeling

// File › Open...
open("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit_binary__nuclei.tif");
// Image › Rename...
rename("binary");

// 4-connected

// Plugins › MorphoLibJ › Binary Images › Connected Components Labeling
run("Connected Components Labeling", "connectivity=4 type=[8 bits]");
// Image › Lookup Tables › glasbey_on_dark
run("glasbey_on_dark");
// Image › Adjust › Brightness/Contrast...
setMinAndMax(0, 20);

// 8-connected

selectWindow("binary");
run("Connected Components Labeling", "connectivity=8 type=[8 bits]");
run("glasbey_on_dark");
setMinAndMax(0, 20);

run("Tile");

// 3D connected components labeling (6-connected)

open("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz_8bit_binary__spots.tif");
rename("binary");
run("Connected Components Labeling", "connectivity=6 type=[8 bits]");
run("glasbey_on_dark");
setMinAndMax(0, 255); // Note: surprisingly this determines the content of below histogram!
run("Histogram", "stack");

skimage napari

# Import modules
import napari
from skimage.io import imread

# Instantiate the napari viewer
viewer = napari.Viewer()

# Read a binary 2D image
binary_2D_image = imread('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit_binary__nuclei.tif')
viewer.add_image(binary_2D_image)

# Connected components with connectivity 1 (aka 2D 4 connectivity) 
from skimage import measure
labels_2D_conn1_image = measure.label(binary_2D_image, connectivity=1)
viewer.add_labels(labels_2D_conn1_image)

# Connected components with connectivity 2 (aka 2D 8 connectivity) 
labels_2D_conn2_image = measure.label(binary_2D_image, connectivity=2)
viewer.add_labels(labels_2D_conn2_image)

# Read a binary 3D image
binary_3D_image = imread('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz_8bit_binary__spots.tif')

# Connected components with connectivity 2 (aka 3D 26 connectivity) 
labels_3D_conn2_image = measure.label(binary_3D_image, connectivity=2)
viewer.add_labels(labels_3D_conn2_image)

# Interrogate the values in the 3D label image
print(np.unique(labels_3D_conn2_image)) # the object indices
print(len(np.unique(labels_3D_conn2_image))-1) # the number of objects (minus background)
print(np.max(labels_3D_conn2_image)) # the number of objects (minus background) (if the labels are consecutive!)
np.sum(labels_3D_conn2_image==2) # the number of pixels (~volume) in object number 2

Exercises

ImageJ MorpholibJ Macro & GUI

• Open xy_8bit_binary__many_vesicles.tif
• Create a label mask image (beware the output data type, because there are quite many objects).
• Count the number of objects in the label mask image.

Solution

/**
 * Required update sites: 
 *   - IJPB-Plugins (MorpholibJ)
 */

run("Close All");

// File › Open...
open("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit_binary__many_vesicles.tif");
run("Connected Components Labeling", "connectivity=4 type=[16 bits]");
run("glasbey_on_dark");
run("Histogram");

Assessment

Fill in the blanks

Fill in the blanks, using these words: less, larger, 6, 255, 4, more.

1. In 3D, pixels have _____ neighbors than in 2D.
2. 8-connected connectivity results in _____ objects than 4-connected connectivity.
3. In 3D, pixels have __ non-diagonal neighbors.
4. In 2D, pixels have __ non-diagonal neighbors.
5. A 8-bit label mask image can have maximally _____ objects.
6. The maximum value in a label mask image is equal to or _____ than the number of objects.

Solution

1. more
2. less
3. 6
4. 4
5. 255
6. larger

Follow-up material

Recommended follow-up modules:

Learn more:

• Wikipedia: Connected components labeling