# Connected Component Analysis

Estimated time 125 minutes

## Overview

### Questions

• How to extract separate objects from an image and describe these objects quantitatively.

### Objectives

• Understand the term object in the context of images.
• Learn how Connected Component Analysis (CCA) works.
• Use CCA to produce an image that highlights every object in a different colour.
• Characterise each object with numbers that describe its appearance.

## Objects

In the Thresholding episode we have covered dividing an image into foreground and background pixels. In the shapes example image, we considered the coloured shapes as foreground objects on a white background.

In thresholding we went from the original image to this version:

Here, we created a mask that only highlights the parts of the image that we find interesting, the objects. All objects have pixel value of True while the background pixels are False.

By looking at the mask image, one can count the objects that are present in the image (7). But how did we actually do that, how did we decide which lump of pixels constitutes a single object?

## Pixel Neighborhoods

In order to decide which pixels belong to the same object, one can exploit their neighborhood: pixels that are directly next to each other and belong to the foreground class can be considered to belong to the same object.

Let’s discuss the concept of pixel neighborhoods in more detail. Consider the following mask “image” with 8 rows, and 8 columns. For the purpose of illustration, the digit 0 is used to represent background pixels, and the letter X is used to represent object pixels foreground).

### OUTPUT

0 0 0 0 0 0 0 0
0 X X 0 0 0 0 0
0 X X 0 0 0 0 0
0 0 0 X X X 0 0
0 0 0 X X X X 0
0 0 0 0 0 0 0 0

The pixels are organised in a rectangular grid. In order to understand pixel neighborhoods we will introduce the concept of “jumps” between pixels. The jumps follow two rules: First rule is that one jump is only allowed along the column, or the row. Diagonal jumps are not allowed. So, from a centre pixel, denoted with o, only the pixels indicated with a 1 are reachable:

### OUTPUT

- 1 -
1 o 1
- 1 -

The pixels on the diagonal (from o) are not reachable with a single jump, which is denoted by the -. The pixels reachable with a single jump form the 1-jump neighborhood.

The second rule states that in a sequence of jumps, one may only jump in row and column direction once -> they have to be orthogonal. An example of a sequence of orthogonal jumps is shown below. Starting from o the first jump goes along the row to the right. The second jump then goes along the column direction up. After this, the sequence cannot be continued as a jump has already been made in both row and column direction.

### OUTPUT

- - 2
- o 1
- - -

All pixels reachable with one, or two jumps form the 2-jump neighborhood. The grid below illustrates the pixels reachable from the centre pixel o with a single jump, highlighted with a 1, and the pixels reachable with 2 jumps with a 2.

### OUTPUT

2 1 2
1 o 1
2 1 2

We want to revisit our example image mask from above and apply the two different neighborhood rules. With a single jump connectivity for each pixel, we get two resulting objects, highlighted in the image with A’s and B’s.

### OUTPUT

0 0 0 0 0 0 0 0
0 A A 0 0 0 0 0
0 A A 0 0 0 0 0
0 0 0 B B B 0 0
0 0 0 B B B B 0
0 0 0 0 0 0 0 0

In the 1-jump version, only pixels that have direct neighbors along rows or columns are considered connected. Diagonal connections are not included in the 1-jump neighborhood. With two jumps, however, we only get a single object A because pixels are also considered connected along the diagonals.

### OUTPUT

0 0 0 0 0 0 0 0
0 A A 0 0 0 0 0
0 A A 0 0 0 0 0
0 0 0 A A A 0 0
0 0 0 A A A A 0
0 0 0 0 0 0 0 0

### Object counting (optional, not included in timing)

How many objects with 1 orthogonal jump, how many with 2 orthogonal jumps?

### OUTPUT

0 0 0 0 0 0 0 0
0 X 0 0 0 X X 0
0 0 X 0 0 0 0 0
0 X 0 X X X 0 0
0 X 0 X X 0 0 0
0 0 0 0 0 0 0 0

1 jump

1. 1
2. 5
3. 2
1. 5

2 jumps

1. 2
2. 3
3. 5
1. 2

### Jumps and neighborhoods

We have just introduced how you can reach different neighboring pixels by performing one or more orthogonal jumps. We have used the terms 1-jump and 2-jump neighborhood. There is also a different way of referring to these neighborhoods: the 4- and 8-neighborhood. With a single jump you can reach four pixels from a given starting pixel. Hence, the 1-jump neighborhood corresponds to the 4-neighborhood. When two orthogonal jumps are allowed, eight pixels can be reached, so the 2-jump neighborhood corresponds to the 8-neighborhood.

## Connected Component Analysis

In order to find the objects in an image, we want to employ an operation that is called Connected Component Analysis (CCA). This operation takes a binary image as an input. Usually, the False value in this image is associated with background pixels, and the True value indicates foreground, or object pixels. Such an image can be produced, e.g., with thresholding. Given a thresholded image, the connected component analysis produces a new labeled image with integer pixel values. Pixels with the same value, belong to the same object. scikit-image provides connected component analysis in the function ski.measure.label(). Let us add this function to the already familiar steps of thresholding an image.

First, import the packages needed for this episode:

### PYTHON

import imageio.v3 as iio
import ipympl
import matplotlib.pyplot as plt
import numpy as np
import skimage as ski

%matplotlib widget

In this episode, we will use the ski.measure.label function to perform the CCA.

Next, we define a reusable Python function connected_components:

### PYTHON

def connected_components(filename, sigma=1.0, t=0.5, connectivity=2):
# convert the image to grayscale
gray_image = ski.color.rgb2gray(image)
# denoise the image with a Gaussian filter
blurred_image = ski.filters.gaussian(gray_image, sigma=sigma)
# mask the image according to threshold
# perform connected component analysis
connectivity=connectivity, return_num=True)
return labeled_image, count

The first four lines of code are familiar from the Thresholding episode.

Then we call the ski.measure.label function. This function has one positional argument where we pass the binary_mask, i.e., the binary image to work on. With the optional argument connectivity, we specify the neighborhood in units of orthogonal jumps. For example, by setting connectivity=2 we will consider the 2-jump neighborhood introduced above. The function returns a labeled_image where each pixel has a unique value corresponding to the object it belongs to. In addition, we pass the optional parameter return_num=True to return the maximum label index as count.

### Optional parameters and return values

The optional parameter return_num changes the data type that is returned by the function ski.measure.label. The number of labels is only returned if return_num is True. Otherwise, the function only returns the labeled image. This means that we have to pay attention when assigning the return value to a variable. If we omit the optional parameter return_num or pass return_num=False, we can call the function as

### PYTHON

labeled_image = ski.measure.label(binary_mask)

If we pass return_num=True, the function returns a tuple and we can assign it as

### PYTHON

labeled_image, count = ski.measure.label(binary_mask, return_num=True)

If we used the same assignment as in the first case, the variable labeled_image would become a tuple, in which labeled_image is the image and labeled_image is the number of labels. This could cause confusion if we assume that labeled_image only contains the image and pass it to other functions. If you get an AttributeError: 'tuple' object has no attribute 'shape' or similar, check if you have assigned the return values consistently with the optional parameters.

We can call the above function connected_components and display the labeled image like so:

### PYTHON

labeled_image, count = connected_components(filename="data/shapes-01.jpg", sigma=2.0, t=0.9, connectivity=2)

fig, ax = plt.subplots()
plt.imshow(labeled_image)
plt.axis("off");

Here you might get a warning UserWarning: Low image data range; displaying image with stretched contrast. or just see an all black image (Note: this behavior might change in future versions or not occur with a different image viewer).

What went wrong? When you hover over the black image, the pixel values are shown as numbers in the lower corner of the viewer. You can see that some pixels have values different from 0, so they are not actually pure black. Let’s find out more by examining labeled_image. Properties that might be interesting in this context are dtype, the minimum and maximum value. We can print them with the following lines:

### PYTHON

print("dtype:", labeled_image.dtype)
print("min:", np.min(labeled_image))
print("max:", np.max(labeled_image))

Examining the output can give us a clue why the image appears black.

### OUTPUT

dtype: int32
min: 0
max: 11

The dtype of labeled_image is int64. This means that values in this image range from -2 ** 63 to 2 ** 63 - 1. Those are really big numbers. From this available space we only use the range from 0 to 11. When showing this image in the viewer, it squeezes the complete range into 256 gray values. Therefore, the range of our numbers does not produce any visible change.

Fortunately, the scikit-image library has tools to cope with this situation.

We can use the function ski.color.label2rgb() to convert the colours in the image (recall that we already used the ski.color.rgb2gray() function to convert to grayscale). With ski.color.label2rgb(), all objects are coloured according to a list of colours that can be customised. We can use the following commands to convert and show the image:

### PYTHON

# convert the label image to color image
colored_label_image = ski.color.label2rgb(labeled_image, bg_label=0)

fig, ax = plt.subplots()
plt.imshow(colored_label_image)
plt.axis("off");

### How many objects are in that image (15 min)

Now, it is your turn to practice. Using the function connected_components, find two ways of printing out the number of objects found in the image.

What number of objects would you expect to get?

How does changing the sigma and threshold values influence the result?

As you might have guessed, the return value count already contains the number of found images. So it can simply be printed with

### PYTHON

print("Found", count, "objects in the image.")

But there is also a way to obtain the number of found objects from the labeled image itself. Recall that all pixels that belong to a single object are assigned the same integer value. The connected component algorithm produces consecutive numbers. The background gets the value 0, the first object gets the value 1, the second object the value 2, and so on. This means that by finding the object with the maximum value, we also know how many objects there are in the image. We can thus use the np.max function from NumPy to find the maximum value that equals the number of found objects:

### PYTHON

num_objects = np.max(labeled_image)
print("Found", num_objects, "objects in the image.")

Invoking the function with sigma=2.0, and threshold=0.9, both methods will print

### OUTPUT

Found 11 objects in the image.

Lowering the threshold will result in fewer objects. The higher the threshold is set, the more objects are found. More and more background noise gets picked up as objects. Larger sigmas produce binary masks with less noise and hence a smaller number of objects. Setting sigma too high bears the danger of merging objects.

You might wonder why the connected component analysis with sigma=2.0, and threshold=0.9 finds 11 objects, whereas we would expect only 7 objects. Where are the four additional objects? With a bit of detective work, we can spot some small objects in the image, for example, near the left border.

For us it is clear that these small spots are artifacts and not objects we are interested in. But how can we tell the computer? One way to calibrate the algorithm is to adjust the parameters for blurring (sigma) and thresholding (t), but you may have noticed during the above exercise that it is quite hard to find a combination that produces the right output number. In some cases, background noise gets picked up as an object. And with other parameters, some of the foreground objects get broken up or disappear completely. Therefore, we need other criteria to describe desired properties of the objects that are found.

## Morphometrics - Describe object features with numbers

Morphometrics is concerned with the quantitative analysis of objects and considers properties such as size and shape. For the example of the images with the shapes, our intuition tells us that the objects should be of a certain size or area. So we could use a minimum area as a criterion for when an object should be detected. To apply such a criterion, we need a way to calculate the area of objects found by connected components. Recall how we determined the root mass in the Thresholding episode by counting the pixels in the binary mask. But here we want to calculate the area of several objects in the labeled image. The scikit-image library provides the function ski.measure.regionprops to measure the properties of labeled regions. It returns a list of RegionProperties that describe each connected region in the images. The properties can be accessed using the attributes of the RegionProperties data type. Here we will use the properties "area" and "label". You can explore the scikit-image documentation to learn about other properties available.

We can get a list of areas of the labeled objects as follows:

### PYTHON

# compute object features and extract object areas
object_features = ski.measure.regionprops(labeled_image)
object_areas = [objf["area"] for objf in object_features]
object_areas

This will produce the output

### OUTPUT

[318542, 1, 523204, 496613, 517331, 143, 256215, 1, 68, 338784, 265755]

### Plot a histogram of the object area distribution (10 min)

Similar to how we determined a “good” threshold in the Thresholding episode, it is often helpful to inspect the histogram of an object property. For example, we want to look at the distribution of the object areas.

1. Create and examine a histogram of the object areas obtained with ski.measure.regionprops.
2. What does the histogram tell you about the objects?

The histogram can be plotted with

### PYTHON

fig, ax = plt.subplots()
plt.hist(object_areas)
plt.xlabel("Area (pixels)")
plt.ylabel("Number of objects");

The histogram shows the number of objects (vertical axis) whose area is within a certain range (horizontal axis). The height of the bars in the histogram indicates the prevalence of objects with a certain area. The whole histogram tells us about the distribution of object sizes in the image. It is often possible to identify gaps between groups of bars (or peaks if we draw the histogram as a continuous curve) that tell us about certain groups in the image.

In this example, we can see that there are four small objects that contain less than 50000 pixels. Then there is a group of four (1+1+2) objects in the range between 200000 and 400000, and three objects with a size around 500000. For our object count, we might want to disregard the small objects as artifacts, i.e, we want to ignore the leftmost bar of the histogram. We could use a threshold of 50000 as the minimum area to count. In fact, the object_areas list already tells us that there are fewer than 200 pixels in these objects. Therefore, it is reasonable to require a minimum area of at least 200 pixels for a detected object. In practice, finding the “right” threshold can be tricky and usually involves an educated guess based on domain knowledge.

### Filter objects by area (10 min)

Now we would like to use a minimum area criterion to obtain a more accurate count of the objects in the image.

1. Find a way to calculate the number of objects by only counting objects above a certain area.

One way to count only objects above a certain area is to first create a list of those objects, and then take the length of that list as the object count. This can be done as follows:

### PYTHON

min_area = 200
large_objects = []
for objf in object_features:
if objf["area"] > min_area:
large_objects.append(objf["label"])
print("Found", len(large_objects), "objects!")

Another option is to use NumPy arrays to create the list of large objects. We first create an array object_areas containing the object areas, and an array object_labels containing the object labels. The labels of the objects are also returned by ski.measure.regionprops. We have already seen that we can create boolean arrays using comparison operators. Here we can use object_areas > min_area to produce an array that has the same dimension as object_labels. It can then used to select the labels of objects whose area is greater than min_area by indexing:

### PYTHON

object_areas = np.array([objf["area"] for objf in object_features])
object_labels = np.array([objf["label"] for objf in object_features])
large_objects = object_labels[object_areas > min_area]
print("Found", len(large_objects), "objects!")

The advantage of using NumPy arrays is that for loops and if statements in Python can be slow, and in practice the first approach may not be feasible if the image contains a large number of objects. In that case, NumPy array functions turn out to be very useful because they are much faster.

In this example, we can also use the np.count_nonzero function that we have seen earlier together with the > operator to count the objects whose area is above min_area.

### PYTHON

n = np.count_nonzero(object_areas > min_area)
print("Found", n, "objects!")

For all three alternatives, the output is the same and gives the expected count of 7 objects.

### Using functions from NumPy and other Python packages

Functions from Python packages such as NumPy are often more efficient and require less code to write. It is a good idea to browse the reference pages of numpy and skimage to look for an availabe function that can solve a given task.

### Remove small objects (20 min)

We might also want to exclude (mask) the small objects when plotting the labeled image.

1. Enhance the connected_components function such that it automatically removes objects that are below a certain area that is passed to the function as an optional parameter.

To remove the small objects from the labeled image, we change the value of all pixels that belong to the small objects to the background label 0. One way to do this is to loop over all objects and set the pixels that match the label of the object to 0.

### PYTHON

for object_id, objf in enumerate(object_features, start=1):
if objf["area"] < min_area:
labeled_image[labeled_image == objf["label"]] = 0

Here NumPy functions can also be used to eliminate for loops and if statements. Like above, we can create an array of the small object labels with the comparison object_areas < min_area. We can use another NumPy function, np.isin, to set the pixels of all small objects to 0. np.isin takes two arrays and returns a boolean array with values True if the entry of the first array is found in the second array, and False otherwise. This array can then be used to index the labeled_image and set the entries that belong to small objects to 0.

### PYTHON

object_areas = np.array([objf["area"] for objf in object_features])
object_labels = np.array([objf["label"] for objf in object_features])
small_objects = object_labels[object_areas < min_area]
labeled_image[np.isin(labeled_image,small_objects)] = 0

An even more elegant way to remove small objects from the image is to leverage the ski.morphology module. It provides a function ski.morphology.remove_small_objects that does exactly what we are looking for. It can be applied to a binary image and returns a mask in which all objects smaller than min_area are excluded, i.e, their pixel values are set to False. We can then apply ski.measure.label to the masked image:

### PYTHON

object_mask = ski.morphology.remove_small_objects(binary_mask,min_area)
connectivity=connectivity, return_num=True)

Using the scikit-image features, we can implement the enhanced_connected_component as follows:

### PYTHON

def enhanced_connected_components(filename, sigma=1.0, t=0.5, connectivity=2, min_area=0):
gray_image = ski.color.rgb2gray(image)
blurred_image = ski.filters.gaussian(gray_image, sigma=sigma)
connectivity=connectivity, return_num=True)
return labeled_image, count

We can now call the function with a chosen min_area and display the resulting labeled image:

### PYTHON

labeled_image, count = enhanced_connected_components(filename="data/shapes-01.jpg", sigma=2.0, t=0.9,
connectivity=2, min_area=min_area)
colored_label_image = ski.color.label2rgb(labeled_image, bg_label=0)

fig, ax = plt.subplots()
plt.imshow(colored_label_image)
plt.axis("off");

print("Found", count, "objects in the image.")

### OUTPUT

Found 7 objects in the image.

Note that the small objects are “gone” and we obtain the correct number of 7 objects in the image.

### Colour objects by area (optional, not included in timing)

Finally, we would like to display the image with the objects coloured according to the magnitude of their area. In practice, this can be used with other properties to give visual cues of the object properties.

We already know how to get the areas of the objects from the regionprops. We just need to insert a zero area value for the background (to colour it like a zero size object). The background is also labeled 0 in the labeled_image, so we insert the zero area value in front of the first element of object_areas with np.insert. Then we can create a colored_area_image where we assign each pixel value the area by indexing the object_areas with the label values in labeled_image.

### PYTHON

object_areas = np.array([objf["area"] for objf in ski.measure.regionprops(labeled_image)])
object_areas = np.insert(0,1,object_areas)
colored_area_image = object_areas[labeled_image]

fig, ax = plt.subplots()
im = plt.imshow(colored_area_image)
cbar = fig.colorbar(im, ax=ax, shrink=0.85)
cbar.ax.set_title("Area")
plt.axis("off");

### Callout

You may have noticed that in the solution, we have used the labeled_image to index the array object_areas. This is an example of advanced indexing in NumPy The result is an array of the same shape as the labeled_image whose pixel values are selected from object_areas according to the object label. Hence the objects will be colored by area when the result is displayed. Note that advanced indexing with an integer array works slightly different than the indexing with a Boolean array that we have used for masking. While Boolean array indexing returns only the entries corresponding to the True values of the index, integer array indexing returns an array with the same shape as the index. You can read more about advanced indexing in the NumPy documentation.

### Keypoints

• We can use ski.measure.label to find and label connected objects in an image.
• We can use ski.measure.regionprops to measure properties of labeled objects.
• We can use ski.morphology.remove_small_objects to mask small objects and remove artifacts from an image.
• We can display the labeled image to view the objects coloured by label.