Statistics using SimpleITK#

We can use Simple for extracting features from label images. For convenience reasons we use the napari-simpleitk-image-processing library.

import numpy as np
import pandas as pd
from skimage.io import imread
from pyclesperanto_prototype import imshow
from napari_simpleitk_image_processing import watershed_otsu_labeling
from napari_simpleitk_image_processing import label_statistics
blobs = imread('../../data/blobs.tif')
imshow(blobs)
../_images/statistics_with_simpleitk_2_0.png

Starting point: a segmented label image#

labels = watershed_otsu_labeling(blobs)
imshow(labels, labels=True)
../_images/statistics_with_simpleitk_4_0.png

Label statistics#

statistics = label_statistics(blobs, labels, None, True, True, True, True, True, True)

df = pd.DataFrame(statistics)
df
label maximum mean median minimum sigma sum variance bbox_0 bbox_1 ... number_of_pixels_on_border perimeter perimeter_on_border perimeter_on_border_ratio principal_axes0 principal_axes1 principal_axes2 principal_axes3 principal_moments0 principal_moments1
0 1 224.0 137.526132 136.0 112.0 13.360739 157880.0 178.509343 0 0 ... 36 2461.579651 36.0 0.014625 -0.574118 -0.818773 0.818773 -0.574118 5063.911496 5560.184284
1 2 232.0 193.014354 200.0 128.0 28.559077 80680.0 815.620897 11 0 ... 16 85.499572 16.0 0.187135 0.902494 0.430703 -0.430703 0.902494 17.056706 72.884853
2 3 224.0 179.846995 184.0 128.0 21.328889 32912.0 454.921516 53 0 ... 21 53.456120 21.0 0.392846 -0.042759 -0.999085 0.999085 -0.042759 8.637199 27.432794
3 4 248.0 207.082171 216.0 120.0 27.772832 133568.0 771.330194 95 0 ... 23 93.409370 23.0 0.246228 0.991601 0.129334 -0.129334 0.991601 48.975064 55.851742
4 5 248.0 223.146402 232.0 128.0 30.246515 89928.0 914.851647 144 0 ... 19 74.218143 19.0 0.256002 0.974707 0.223487 -0.223487 0.974707 32.059800 33.765222
5 6 248.0 214.906725 224.0 128.0 26.386796 99072.0 696.263020 238 0 ... 39 80.787183 40.0 0.495128 0.999408 0.034407 -0.034407 0.999408 23.320204 59.820502
6 7 248.0 211.565891 224.0 136.0 30.197236 54584.0 911.873073 189 7 ... 0 57.938471 0.0 0.000000 0.932037 0.362364 -0.362364 0.932037 18.705896 22.720115
7 8 200.0 166.171429 168.0 136.0 16.466894 11632.0 271.158592 133 17 ... 0 29.917295 0.0 0.000000 0.960385 0.278678 -0.278678 0.960385 4.741825 6.584094
8 9 224.0 176.932331 176.0 128.0 24.022064 47064.0 577.059555 211 17 ... 0 59.834590 0.0 0.000000 0.997651 0.068495 -0.068495 0.997651 14.969363 30.395186
9 10 240.0 191.598174 200.0 128.0 28.239851 41960.0 797.489171 37 18 ... 0 53.360835 0.0 0.000000 0.992904 0.118917 -0.118917 0.992904 15.331341 19.871929
10 11 232.0 190.962963 200.0 128.0 23.617106 92808.0 557.767698 162 21 ... 0 79.485892 0.0 0.000000 0.950824 0.309732 -0.309732 0.950824 35.879673 42.001599
11 12 232.0 173.000000 176.0 128.0 19.441981 110720.0 377.990610 59 26 ... 0 93.218800 0.0 0.000000 0.848443 0.529287 -0.529287 0.848443 38.069371 69.135089
12 13 200.0 168.351648 168.0 128.0 19.226356 15320.0 369.652747 3 39 ... 0 35.050291 0.0 0.000000 0.945102 0.326774 -0.326774 0.945102 5.568379 9.510959
13 14 248.0 203.019608 208.0 128.0 32.682072 41416.0 1068.117840 226 39 ... 0 51.464716 0.0 0.000000 0.999604 -0.028149 0.028149 0.999604 13.550254 19.604062
14 15 248.0 216.156863 224.0 136.0 28.644305 88192.0 820.496218 129 42 ... 0 72.226739 0.0 0.000000 0.985169 -0.171585 0.171585 0.985169 26.472262 39.901074
15 16 232.0 181.963255 184.0 120.0 24.411574 69328.0 595.924962 185 43 ... 0 81.382011 0.0 0.000000 0.925427 0.378925 -0.378925 0.925427 12.588614 76.471615
16 17 248.0 197.333333 200.0 120.0 26.311828 97088.0 692.312288 17 44 ... 0 79.485892 0.0 0.000000 0.949997 0.312260 -0.312260 0.949997 33.707850 45.675040
17 18 248.0 200.160401 208.0 128.0 27.596051 79864.0 761.542046 90 60 ... 0 72.782100 0.0 0.000000 0.959680 0.281094 -0.281094 0.959680 21.950618 46.037485
18 19 248.0 205.943775 216.0 136.0 30.086114 51280.0 905.174245 207 61 ... 0 57.153073 0.0 0.000000 0.954815 0.297199 -0.297199 0.954815 14.780837 26.562206
19 20 232.0 192.235988 200.0 120.0 25.416595 65168.0 646.003316 235 63 ... 11 66.863705 11.0 0.164514 -0.593398 -0.804909 0.804909 -0.593398 22.740379 32.242766
20 21 224.0 189.577465 200.0 136.0 24.291178 26920.0 590.061333 162 66 ... 0 42.864805 0.0 0.000000 0.946821 0.321762 -0.321762 0.946821 9.729421 13.167326
21 22 224.0 187.870324 192.0 128.0 24.658125 75336.0 608.023142 53 73 ... 0 71.996702 0.0 0.000000 0.993149 -0.116857 0.116857 0.993149 28.718371 35.697584
22 23 248.0 208.960836 216.0 128.0 30.936449 80032.0 957.063907 117 76 ... 0 79.485892 0.0 0.000000 -0.249601 -0.968349 0.968349 -0.249601 17.353521 61.813901
23 24 240.0 182.081301 184.0 128.0 26.433619 44792.0 698.736220 26 77 ... 0 56.597713 0.0 0.000000 0.999988 -0.004899 0.004899 0.999988 16.419754 23.402458
24 25 248.0 199.629032 200.0 128.0 27.426587 99016.0 752.217660 0 82 ... 29 84.484136 29.0 0.343260 0.998757 -0.049841 0.049841 0.998757 24.491079 67.691848
25 26 240.0 192.449438 192.0 120.0 26.732458 51384.0 714.624314 214 86 ... 0 58.493832 0.0 0.000000 0.929427 0.369006 -0.369006 0.929427 18.770857 24.067028
26 27 240.0 202.691706 208.0 128.0 24.550960 129520.0 602.749633 158 88 ... 0 97.891721 0.0 0.000000 0.252109 -0.967699 0.967699 0.252109 34.790068 80.474893
27 28 232.0 200.641975 208.0 136.0 22.958406 32504.0 527.088413 245 95 ... 20 51.004641 20.0 0.392121 0.999016 0.044348 -0.044348 0.999016 7.500566 24.475429
28 29 232.0 201.661721 208.0 136.0 24.593688 67960.0 604.849513 92 102 ... 0 66.308345 0.0 0.000000 0.159256 -0.987237 0.987237 0.159256 22.243114 32.490995
29 30 240.0 202.541910 208.0 136.0 26.036533 103904.0 677.901072 26 108 ... 0 82.167409 0.0 0.000000 0.989629 0.143647 -0.143647 0.989629 32.933086 51.101812
30 31 232.0 192.649396 200.0 120.0 25.700290 111544.0 660.504892 113 109 ... 0 105.610950 0.0 0.000000 0.903873 0.427801 -0.427801 0.903873 18.013291 134.834719
31 32 248.0 195.953871 200.0 120.0 30.668210 118944.0 940.539123 151 114 ... 0 99.692555 0.0 0.000000 0.830015 -0.557742 0.557742 0.830015 25.231170 101.168109
32 33 232.0 184.131148 192.0 128.0 26.165665 33696.0 684.642046 56 115 ... 0 49.568597 0.0 0.000000 0.991750 -0.128190 0.128190 0.991750 12.926033 16.531401
33 34 248.0 215.125448 224.0 128.0 30.288772 120040.0 917.409728 211 115 ... 0 85.174249 0.0 0.000000 0.220924 -0.975291 0.975291 0.220924 40.823045 48.505271
34 35 176.0 151.529412 152.0 136.0 15.091875 2576.0 227.764706 251 124 ... 2 14.843629 2.0 0.134738 -0.163228 -0.986588 0.986588 -0.163228 1.046852 1.755916
35 36 232.0 191.224806 200.0 120.0 25.409101 49336.0 645.622417 0 130 ... 13 57.153073 13.0 0.227459 0.970628 0.240586 -0.240586 0.970628 16.246640 26.130982
36 37 248.0 201.665497 200.0 128.0 28.167372 172424.0 793.400857 76 137 ... 0 121.660584 0.0 0.000000 0.975948 0.218005 -0.218005 0.975948 27.394927 179.878327
37 38 248.0 208.017467 208.0 128.0 29.009010 95272.0 841.522670 233 137 ... 23 83.278130 23.0 0.276183 0.844799 0.535083 -0.535083 0.844799 21.736051 65.950501
38 39 232.0 197.419355 208.0 128.0 28.666250 42840.0 821.753883 60 138 ... 0 52.805475 0.0 0.000000 0.986995 0.160753 -0.160753 0.986995 14.052349 21.247148
39 40 232.0 191.774648 200.0 136.0 24.532044 27232.0 601.821197 183 149 ... 0 42.309444 0.0 0.000000 0.965804 0.259275 -0.259275 0.965804 9.197500 13.952669
40 41 232.0 180.952381 184.0 128.0 24.471401 72200.0 598.849486 125 152 ... 0 72.226739 0.0 0.000000 -0.653319 -0.757082 0.757082 -0.653319 22.888892 44.383172
41 42 248.0 204.708333 216.0 120.0 28.500531 78608.0 812.280244 6 157 ... 0 70.100583 0.0 0.000000 0.904586 0.426291 -0.426291 0.904586 25.066435 37.439939
42 43 248.0 191.221239 192.0 128.0 26.451712 43216.0 699.693058 206 158 ... 0 53.916196 0.0 0.000000 0.977778 0.209643 -0.209643 0.977778 15.830580 20.544723
43 44 240.0 200.091691 208.0 136.0 27.115603 69832.0 735.255937 36 160 ... 0 66.863705 0.0 0.000000 0.999560 -0.029663 0.029663 0.999560 21.238298 36.355055
44 45 240.0 204.294599 216.0 128.0 28.702058 124824.0 823.808151 167 167 ... 0 88.736449 0.0 0.000000 0.984212 -0.176992 0.176992 0.984212 44.044878 54.215343
45 46 248.0 215.719298 224.0 136.0 32.074924 73776.0 1028.800741 223 174 ... 0 65.752985 0.0 0.000000 0.756607 0.653870 -0.653870 0.756607 24.162673 30.794348
46 47 240.0 205.387454 216.0 136.0 24.250565 111320.0 588.089898 117 185 ... 0 83.833491 0.0 0.000000 0.116985 -0.993134 0.993134 0.116985 41.327753 45.322011
47 48 232.0 194.711864 200.0 136.0 29.510402 11488.0 870.863822 251 193 ... 15 33.479495 15.0 0.448035 0.999821 0.018944 -0.018944 0.999821 1.690956 14.325132
48 49 232.0 186.684211 192.0 128.0 25.196557 28376.0 634.866504 200 195 ... 0 44.435601 0.0 0.000000 0.980620 0.195921 -0.195921 0.980620 9.610562 15.277380
49 50 232.0 168.875000 168.0 120.0 25.410148 75656.0 645.675615 18 200 ... 0 82.262694 0.0 0.000000 0.958423 -0.285352 0.285352 0.958423 17.922660 72.404250
50 51 248.0 229.430657 248.0 144.0 26.958373 125728.0 726.753866 90 200 ... 0 85.078964 0.0 0.000000 0.774700 -0.632329 0.632329 0.774700 31.162401 61.364008
51 52 216.0 191.100952 200.0 128.0 22.709233 100328.0 515.709255 51 203 ... 0 83.278130 0.0 0.000000 0.957483 -0.288491 0.288491 0.957483 31.380235 55.869804
52 53 240.0 206.313514 216.0 144.0 26.433038 38168.0 698.705523 0 214 ... 24 56.137637 24.0 0.427521 0.999497 -0.031726 0.031726 0.999497 6.941190 35.497670
53 54 248.0 224.251473 240.0 144.0 29.542436 114144.0 872.755534 221 215 ... 0 80.826651 0.0 0.000000 0.327038 -0.945011 0.945011 0.327038 37.923490 43.556039
54 55 248.0 200.145985 208.0 128.0 27.374216 164520.0 749.347724 161 217 ... 0 108.157714 0.0 0.000000 0.119267 -0.992862 0.992862 0.119267 41.764270 104.664511
55 56 232.0 195.920949 200.0 128.0 25.860310 49568.0 668.755631 131 224 ... 0 56.597713 0.0 0.000000 0.998990 -0.044926 0.044926 0.998990 15.823917 25.661186
56 57 224.0 188.994924 192.0 136.0 24.472912 37232.0 598.923443 40 232 ... 0 50.123958 0.0 0.000000 0.989524 0.144369 -0.144369 0.989524 11.684511 21.090000
57 58 216.0 184.761905 192.0 136.0 23.153557 15520.0 536.087206 118 249 ... 21 44.856208 21.0 0.468163 0.006588 -0.999978 0.999978 0.006588 1.724907 28.033313
58 59 248.0 191.314286 184.0 144.0 30.233652 13392.0 914.073706 171 249 ... 17 37.271733 17.0 0.456110 -0.007722 -0.999970 0.999970 -0.007722 1.757346 18.883470
59 60 248.0 204.600000 208.0 152.0 29.429446 8184.0 866.092308 228 250 ... 12 27.005740 12.0 0.444350 -0.034966 -0.999388 0.999388 -0.034966 1.150116 9.234259

60 rows × 33 columns

These are all columns that are available:

print(statistics.keys())
dict_keys(['label', 'maximum', 'mean', 'median', 'minimum', 'sigma', 'sum', 'variance', 'bbox_0', 'bbox_1', 'bbox_2', 'bbox_3', 'centroid_0', 'centroid_1', 'elongation', 'feret_diameter', 'flatness', 'roundness', 'equivalent_ellipsoid_diameter_0', 'equivalent_ellipsoid_diameter_1', 'equivalent_spherical_perimeter', 'equivalent_spherical_radius', 'number_of_pixels', 'number_of_pixels_on_border', 'perimeter', 'perimeter_on_border', 'perimeter_on_border_ratio', 'principal_axes0', 'principal_axes1', 'principal_axes2', 'principal_axes3', 'principal_moments0', 'principal_moments1'])
df.describe()
label maximum mean median minimum sigma sum variance bbox_0 bbox_1 ... number_of_pixels_on_border perimeter perimeter_on_border perimeter_on_border_ratio principal_axes0 principal_axes1 principal_axes2 principal_axes3 principal_moments0 principal_moments1
count 60.000000 60.000000 60.000000 60.000000 60.000000 60.000000 60.000000 60.000000 60.000000 60.000000 ... 60.000000 60.000000 60.000000 60.000000 60.000000 60.000000 60.000000 60.000000 60.000000 60.000000
mean 30.500000 236.000000 194.966598 200.800000 130.000000 26.069709 71696.933333 693.962389 123.516667 110.200000 ... 5.683333 107.224145 5.700000 0.089574 0.696028 -0.138186 0.138186 0.696028 104.628848 134.931776
std 17.464249 14.012101 16.394414 19.579304 7.456086 3.817798 41465.329504 183.657670 81.460709 76.672493 ... 10.255204 309.846236 10.310929 0.161164 0.476871 0.526743 0.526743 0.476871 651.200133 712.958883
min 1.000000 176.000000 137.526132 136.000000 112.000000 13.360739 2576.000000 178.509343 0.000000 0.000000 ... 0.000000 14.843629 0.000000 0.000000 -0.653319 -0.999978 -0.653870 -0.653319 1.046852 1.755916
25% 15.750000 232.000000 188.713774 192.000000 128.000000 24.472535 40604.000000 598.904954 52.500000 42.750000 ... 0.000000 53.221995 0.000000 0.000000 0.649215 -0.663517 -0.279282 0.649215 12.841678 22.351873
50% 30.500000 240.000000 195.937410 200.000000 128.000000 26.409917 71016.000000 697.484271 127.000000 108.500000 ... 0.000000 68.482144 0.000000 0.000000 0.952820 0.026675 -0.026675 0.952820 18.738377 35.597627
75% 45.250000 248.000000 204.627083 210.000000 136.000000 28.649791 99386.000000 820.810634 201.500000 168.750000 ... 11.250000 83.278130 11.250000 0.142182 0.989550 0.279282 0.663517 0.989550 29.329379 56.857478
max 60.000000 248.000000 229.430657 248.000000 152.000000 32.682072 172424.000000 1068.117840 251.000000 250.000000 ... 39.000000 2461.579651 40.000000 0.495128 0.999988 0.653870 0.999978 0.999988 5063.911496 5560.184284

8 rows × 33 columns