Statistics using Scikit-image#

We can use scikit-image for extracting features from label images. For convenience reasons we use the napari-skimage-regionprops library.

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
from napari_skimage_regionprops import regionprops_table
from pyclesperanto_prototype import imshow
import pandas as pd 
import numpy as np
import pyclesperanto_prototype as cle

# 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
imshow(label_image, labels=True)
../_images/5257573298d6a444b6a34d09506d6cf9a10aab208bce52c927a91ea6491b20ee.png

Measurements / region properties#

We are now using the very handy function regionprops_table. It provides features based on the scikit-image regionprops list of measurements library. Let us check first what we need to provide for this function:

regionprops_table?

Signature:
regionprops_table(
    image: 'napari.types.ImageData',
    labels: 'napari.types.LabelsData',
    size: bool = True,
    intensity: bool = True,
    perimeter: bool = False,
    shape: bool = False,
    position: bool = False,
    moments: bool = False,
    napari_viewer: 'napari.Viewer' = None,
) -> 'pandas.DataFrame'
Docstring: Adds a table widget to a given napari viewer with quantitative analysis results derived from an image-label pair.
File:      c:\users\maral\mambaforge\envs\feature_blogpost\lib\site-packages\napari_skimage_regionprops\_regionprops.py
Type:      function

We see that we need to provide which feature categories we want to measure. One way of dividing these categories is shown here:

Feature categories which are set to True are measured by default. In this case, the categories are size and intensity. But perimeter and shape would be also interesting to investigate. So we need to set them to True as well.

df = pd.DataFrame(regionprops_table(image , label_image, 
                                           perimeter = True, 
                                           shape = True, 
                                           position=True,
                                           moments=True))
df
label area bbox_area equivalent_diameter convex_area max_intensity mean_intensity min_intensity perimeter perimeter_crofton ... moments_hu-1 moments_hu-2 moments_hu-3 moments_hu-4 moments_hu-5 moments_hu-6 standard_deviation_intensity aspect_ratio roundness circularity
0 1 429 750 23.371345 479 232.0 191.440559 128.0 89.012193 87.070368 ... 0.018445 0.000847 1.133411e-04 1.205336e-08 -2.714451e-06 3.298765e-08 29.793138 2.088249 0.451572 0.680406
1 2 183 231 15.264430 190 224.0 179.846995 128.0 53.556349 53.456120 ... 0.010549 0.001177 6.734903e-05 -1.483503e-08 -5.401836e-06 -1.180484e-08 21.270534 1.782168 0.530849 0.801750
2 3 658 756 28.944630 673 248.0 205.604863 120.0 95.698485 93.409370 ... 0.000113 0.000104 1.146642e-06 4.801450e-13 9.889775e-09 -1.248247e-11 29.392255 1.067734 0.918683 0.902871
3 4 433 529 23.480049 445 248.0 217.515012 120.0 77.455844 76.114262 ... 0.000096 0.000348 3.349309e-07 -3.612652e-12 2.526209e-09 -9.198337e-14 35.852345 1.061942 0.917813 0.906963
4 5 472 551 24.514670 486 248.0 213.033898 128.0 83.798990 82.127941 ... 0.005876 0.000072 4.356690e-06 -6.223212e-11 -3.178823e-07 4.522442e-11 28.741080 1.579415 0.621952 0.844645
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
57 58 213 285 16.468152 221 224.0 184.525822 120.0 52.284271 52.250114 ... 0.001745 0.000002 1.126452e-07 5.814575e-14 4.625041e-09 -6.410600e-15 28.255467 1.296143 0.771094 0.979146
58 59 79 108 10.029253 84 248.0 184.810127 128.0 39.313708 39.953250 ... 0.056792 0.001777 1.725200e-04 -5.364882e-08 -2.906455e-05 -7.903632e-08 33.739912 3.173540 0.300766 0.642316
59 60 88 110 10.585135 92 216.0 182.727273 128.0 45.692388 46.196967 ... 0.097966 0.002268 2.994111e-04 -1.724577e-08 -3.731912e-05 -2.461160e-07 24.417173 4.021193 0.238521 0.529669
60 61 52 75 8.136858 56 248.0 189.538462 128.0 30.692388 32.924135 ... 0.046813 0.003694 3.041106e-04 -2.539652e-07 -5.698262e-05 -1.984555e-07 37.867411 2.839825 0.322190 0.693668
61 62 48 68 7.817640 53 224.0 173.833333 128.0 33.071068 35.375614 ... 0.125246 0.004295 1.300612e-03 2.775191e-06 3.027878e-04 1.321910e-06 27.987596 4.417297 0.213334 0.551512

62 rows × 86 columns

As you can see, we have now plenty of features to investigate. We can print out all feature names with the keys function:

print(df.keys())

Index(['label', 'area', 'bbox_area', 'equivalent_diameter', 'convex_area',
       'max_intensity', 'mean_intensity', 'min_intensity', 'perimeter',
       'perimeter_crofton', 'extent', 'local_centroid-0', 'local_centroid-1',
       'solidity', 'feret_diameter_max', 'major_axis_length',
       'minor_axis_length', 'orientation', 'eccentricity', 'centroid-0',
       'centroid-1', 'bbox-0', 'bbox-1', 'bbox-2', 'bbox-3',
       'weighted_centroid-0', 'weighted_centroid-1', 'moments-0-0',
       'moments-0-1', 'moments-0-2', 'moments-0-3', 'moments-1-0',
       'moments-1-1', 'moments-1-2', 'moments-1-3', 'moments-2-0',
       'moments-2-1', 'moments-2-2', 'moments-2-3', 'moments-3-0',
       'moments-3-1', 'moments-3-2', 'moments-3-3', 'moments_normalized-0-0',
       'moments_normalized-0-1', 'moments_normalized-0-2',
       'moments_normalized-0-3', 'moments_normalized-1-0',
       'moments_normalized-1-1', 'moments_normalized-1-2',
       'moments_normalized-1-3', 'moments_normalized-2-0',
       'moments_normalized-2-1', 'moments_normalized-2-2',
       'moments_normalized-2-3', 'moments_normalized-3-0',
       'moments_normalized-3-1', 'moments_normalized-3-2',
       'moments_normalized-3-3', 'moments_central-0-0', 'moments_central-0-1',
       'moments_central-0-2', 'moments_central-0-3', 'moments_central-1-0',
       'moments_central-1-1', 'moments_central-1-2', 'moments_central-1-3',
       'moments_central-2-0', 'moments_central-2-1', 'moments_central-2-2',
       'moments_central-2-3', 'moments_central-3-0', 'moments_central-3-1',
       'moments_central-3-2', 'moments_central-3-3', 'moments_hu-0',
       'moments_hu-1', 'moments_hu-2', 'moments_hu-3', 'moments_hu-4',
       'moments_hu-5', 'moments_hu-6', 'standard_deviation_intensity',
       'aspect_ratio', 'roundness', 'circularity'],
      dtype='object')

And describe gives us basic statistics like max, mean, min and std of each feature:

df.describe()
label area bbox_area equivalent_diameter convex_area max_intensity mean_intensity min_intensity perimeter perimeter_crofton ... moments_hu-1 moments_hu-2 moments_hu-3 moments_hu-4 moments_hu-5 moments_hu-6 standard_deviation_intensity aspect_ratio roundness circularity
count 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 62.000000 ... 62.000000 6.200000e+01 6.200000e+01 6.200000e+01 6.200000e+01 6.200000e+01 62.000000 62.000000 62.000000 62.000000
mean 31.500000 355.370968 475.677419 20.074583 372.790323 233.548387 190.429888 125.161290 67.787235 67.071263 ... 0.012854 5.166382e-04 7.053581e-05 5.855846e-08 3.633047e-06 -8.703715e-09 28.689171 1.637991 0.692418 0.894101
std 18.041619 211.367385 300.328169 7.091876 223.801078 19.371838 15.382559 4.602898 25.008581 23.507575 ... 0.027066 1.077748e-03 2.093454e-04 3.687535e-07 3.998053e-05 2.897240e-07 6.127700 0.794366 0.210973 0.183024
min 1.000000 7.000000 9.000000 2.985411 7.000000 152.000000 146.285714 112.000000 6.828427 9.155272 ... 0.000056 2.514436e-07 6.992073e-09 -2.539652e-07 -5.698262e-05 -1.757113e-06 5.598834 1.048053 0.213334 0.529669
25% 16.250000 194.750000 260.000000 15.745692 204.750000 232.000000 182.969505 120.000000 52.602291 52.551616 ... 0.000627 3.032838e-05 1.242304e-07 -1.881559e-13 -1.039790e-08 -7.811552e-13 26.514258 1.168451 0.538616 0.805774
50% 31.500000 366.000000 448.500000 21.585875 376.500000 240.000000 190.749492 128.000000 69.112698 68.204464 ... 0.001971 6.198102e-05 5.957846e-07 6.176303e-14 5.408146e-10 -9.288753e-15 29.017801 1.316003 0.757485 0.925560
75% 46.750000 500.750000 685.500000 25.250050 516.500000 248.000000 199.725305 128.000000 86.097980 84.307520 ... 0.010151 3.475818e-04 3.799021e-05 1.135209e-11 1.484276e-08 1.902152e-12 32.534123 1.769976 0.851463 0.966037
max 62.000000 896.000000 1350.000000 33.776066 977.000000 248.000000 220.026144 136.000000 129.982756 125.912897 ... 0.125246 5.440067e-03 1.300612e-03 2.775191e-06 3.027878e-04 1.321910e-06 38.323999 4.417297 0.974824 1.886542

8 rows × 86 columns

Exercises#

Make a table with only solidity, circularity and roundness.