Quantitative image analysis#
After segmenting and labeling objects in an image, we can measure properties of these objects.
See also
Before we can do measurements, we need an image and a corresponding label_image. Therefore, we recapitulate filtering, thresholding and labeling:
from skimage.io import imread
from skimage import filters
from skimage import measure
import pandas as pd
import numpy as np
import stackview
# load image
image = imread("data/blobs.tif")
# denoising
blurred_image = filters.gaussian(image, sigma=1)
# binarization
threshold = filters.threshold_otsu(blurred_image)
thresholded_image = blurred_image >= threshold
# labeling
label_image = measure.label(thresholded_image)
# visualization
stackview.insight(label_image)
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Measurements / region properties#
To read out properties from regions, we use the regionprops_table function, which directly returns a dictionary that can be converted to a pandas DataFrame:
# analyse objects using regionprops_table
properties = ['label', 'area', 'mean_intensity', 'major_axis_length', 'minor_axis_length']
measurements = measure.regionprops_table(label_image, intensity_image=image, properties=properties)
df = pd.DataFrame(measurements)
df.head()
| label | area | mean_intensity | major_axis_length | minor_axis_length | |
|---|---|---|---|---|---|
| 0 | 1 | 429.0 | 191.440559 | 34.779230 | 16.654732 |
| 1 | 2 | 183.0 | 179.846995 | 20.950530 | 11.755645 |
| 2 | 3 | 658.0 | 205.604863 | 30.198484 | 28.282790 |
| 3 | 4 | 433.0 | 217.515012 | 24.508791 | 23.079220 |
| 4 | 5 | 472.0 | 213.033898 | 31.084766 | 19.681190 |
The result is already a dictionary of arrays, which can directly be used as a pandas DataFrame. The properties available are listed in the documentation of the measure.regionprops function.
We can add custom columns, for example the aspect_ratio:
# add aspect_ratio column
df['aspect_ratio'] = df['major_axis_length'] / df['minor_axis_length']
df.head()
| label | area | mean_intensity | major_axis_length | minor_axis_length | aspect_ratio | |
|---|---|---|---|---|---|---|
| 0 | 1 | 429.0 | 191.440559 | 34.779230 | 16.654732 | 2.088249 |
| 1 | 2 | 183.0 | 179.846995 | 20.950530 | 11.755645 | 1.782168 |
| 2 | 3 | 658.0 | 205.604863 | 30.198484 | 28.282790 | 1.067734 |
| 3 | 4 | 433.0 | 217.515012 | 24.508791 | 23.079220 | 1.061942 |
| 4 | 5 | 472.0 | 213.033898 | 31.084766 | 19.681190 | 1.579415 |
Those dataframes can be saved to disk conveniently:
df.to_csv("blobs_analysis.csv")
Furthermore, one can measure properties from our df table using numpy. For example the mean area:
# measure mean area
np.mean(df['area'])
np.float64(355.3709677419355)
Exercises#
Analyse the loaded blobs image.
How many objects are in it?
How large is the largest object?
What are mean and standard deviation of the image?
What are mean and standard deviation of the area of the segmented objects?