Latest Page Update: 23-09-2026

BLOB Analysis in Python¶

LEGO Classification¶

We will start by trying some BLOB analysis approaches on a photo of some Lego bricks: lego_4_small.png.

Exercise 1: Binary image from original image¶

Read the image, convert it to grayscale and use Otsus method to compute and apply a threshold.

Show the binary image together with the original image.

Note

Remember to make sure that your foreground class = 1 and background class = 0.

Exercise 2: Remove border BLOBs¶

Use segmentation.clear_border to remove border pixels from the binary image.

Exercise 3: Cleaning using morphological operations¶

In order to remove remove noise and close holes, you should do a morphological closing followed by a morphological opening with a disk shaped structuring element with radius 5.

Tip

See Exercise 4b if you are in doubt.

Exercise 4: Find labels¶

The actual connected component analysis / BLOB analysis is performed using measure.label :

label_img = measure.label(img_open)
n_labels = label_img.max()
print(f"Number of labels: {n_labels}")

Exercise 5: Visualize found labels¶

We can use the function label2rbg to create a visualization of the found BLOBS. Show this together with the original image.

Exericse 6: Compute BLOB features¶

It is possible to compute a wide variety of BLOB features using the measure.regionprops function:

region_props = measure.regionprops(label_img)
areas = np.array([prop.area for prop in region_props])
plt.hist(areas, bins=50)
plt.show()

Exercise 7: Exploring BLOB features¶

There is an example program called Ex5-BlobAnalysisInteractive.py in the exercise material folder.

With that program, you can explore different BLOB features interactively. It requires installation of plotly:

conda install -c plotly plotly=6.0.0

Cell counting¶

The goal of this part of the exercise, is to create a small program that can automatically count the number of cell nuclei in an image.

The images used for the exercise is acquired by the Danish company Chemometec using their image-based cytometers. A cytometer is a machine used in many laboratories to do automated cell counting and analysis. An example image can be seen in below where U2OS cells (human bone cells) have been imaged using ultraviolet (UV) microscopy and a fluorescent staining method named DAPI. Using DAPI staining only the cell nuclei are visible which makes the method very suitable for cell counting.

U2OS cells: Left image is acquired using UV microscopy and the right is the corresponding DAPI image.

The raw images from the Cytometer are 1920x1440 pixels and each pixel is 16 bit (values from 0 to 65535). The resolution is 1.11 \(\mu m\) / pixel.

In this exercise, you need to install the python package imagecodecs to read a .tiff file:

conda install imagecodecs

To make it easier to develop the cell counting program we start by working with smaller areas of the raw images. The images are also converted to 8 bit grayscale images:

in_dir = "data/"
img_org = io.imread(in_dir + 'Sample E2 - U2OS DAPI channel.tiff')
# slice to extract smaller image
img_small = img_org[700:1200, 900:1400]
img_gray = img_as_ubyte(img_small) 
io.imshow(img_gray, vmin=0, vmax=150)
plt.title('DAPI Stained U2OS cell nuclei')
io.show()

As can be seen we use slicing to extract a part of the image. You can use vmin and vmax to visualise specific gray scale ranges (0 to 150 in the example above). Adjust these limits to find out where the cell nuclei are most visible.

Initially, we would like to apply a threshold to create a binary image where nuclei are foreground. To select a good threshold, inspect the histogram:

# avoid bin with value 0 due to the very large number of background pixels
plt.hist(img_gray.ravel(), bins=256, range=(1, 100))
io.show()

Exercise 8: Threshold selection¶

Select an appropriate threshold, that seperates nuclei from the background. You can set it manually or use Otsus method.

Show the binary image together with the original image and evaluate if you got the information you wanted in the binary image.

It can be seen that there is some noise (non-nuclei) present and that some nuclei are connected. Nuclei that are overlapping very much should be discarded in the analysis. However, if they are only touching each other a little we can try to separate them. More on this later.

To make the following analysis easier the objects that touches the border should be removed.

Exercise 9: Remove border BLOBS¶

Use segmentation.clear_border to remove border pixels from the binary image.

To be able to analyse the individual objects, the objects should be labelled.

label_img = measure.label(img_c_b)
image_label_overlay = label2rgb(label_img)
show_comparison(img_org, image_label_overlay, 'Found BLOBS')

In this image, each object has a separate color - does it look reasonable?

Exercise 10: BLOB features¶

The task is now to find some object features that identify the cell nuclei and let us remove noise and connected nuclei. We use the function regionprops to compute a set of features for each object:

region_props = measure.regionprops(label_img)

For example can the area of the first object be seen by: print(region_props[0].area).

A quick way to gather all areas:

areas = np.array([prop.area for prop in region_props])

We can try if the area of the objects is enough to remove invalid object. Plot a histogram of all the areas and see if it can be used to identify well separated nuclei from overlapping nuclei and noise. You should probably play around with the number of bins in your histogram plotting function.

Exercise 11: BLOB classification by area¶

Select a minimum and maximum allowed area and use the following to visualise the result:

min_area =
max_area =

# Create a copy of the label_img
label_img_filter = label_img
for region in region_props:
    # Find the areas that do not fit our criteria
    if region.area > max_area or region.area < min_area:
        # set the pixels in the invalid areas to background
        for cords in region.coords:
            label_img_filter[cords[0], cords[1]] = 0
# Create binary image from the filtered label image
i_area = label_img_filter > 0
show_comparison(img_small, i_area, 'Found nuclei based on area')

Can you find an area interval that works well for these nuclei?

Exercise 12: Feature space¶

Extract all the perimeters of the BLOBS:

perimeters = np.array([prop.perimeter for prop in region_props])

Try to plot the areas versus the perimeters.

Exercise 13: BLOB Circularity¶

We should also examine if the shape of the cells can identify them. A good measure of how circular an object is can be computed as:

\[ f_\text{circ} = \frac{4 \pi A}{P^2}, \]

where \(A\) is the object area and \(P\) is the perimeter. A circle has a circularity close to 1, and very-non-circular object have circularity close to 0.

Compute the circularity for all objects and plot a histogram.

Select some appropriate ranges of accepted circularity. Use these ranges to select only the cells with acceptable areas and circularity and show them in an image.

Exercise 14: BLOB circularity and area¶

Try to plot the areas versus the circularity. What do you observe?

Extend your method to return the number (the count) of well-formed nuclei in the image.

Exercise 15: large scale testing¶

Try to test the method on a larger set of training images. Use slicing to select the different regions from the raw image.

Exercise 16: COS7 cell classification¶

Try your method on the Sample G1 - COS7 cells DAPI channel.tiff image. COS7 cells are African Green Monkey Fibroblast-like Kidney Cells used for a variety of research purposes.

Exercise 17: Handling overlap¶

In certain cases cell nuclei are touching and are therefore being treated as one object. It can sometimes be solved using for example the morphological operation opening before the object labelling. The operation erosion can also be used but it changes the object area.

Exam preparation¶

Below are some example exam exercises related to this weeks material. Work with them, and if you have issues or questions, please ask the TAs, as you will not be able to get help after the last exercise round.

02502 Image Analysis Exam Fall 2022: finding mini figures¶

The company, PaintMyMiniz, would like to have a system that can automatically count the number of mini figures standing on a white plate. They have therefore called on your expertise. You decide to solve the task using BLOB analysis. Your first system works like this:

Converts the input photo from RGB to gray scale
Computes a threshold using Otsu's method
Computes a binary image by setting all pixels below the threshold to foreground and the rest to background
Removes BLOBs that are connected to the edges of the image
Computes all BLOBs in the image
Computes the area and the perimeter of all found BLOBs

You start by testing the system on an example photo (data/figures.png).

Exam question 1: You compute the area of all the BLOBs in the image. How many BLOBs have an area larger than 13000 pixels?

Exam question 2: You find the BLOB with the largest area. What is the perimeter of this BLOB?