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)

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.