Quantitative image analysis using SimpleITK

We can also use SimpleITK 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_segment_blobs_and_things_with_membranes import voronoi_otsu_labeling
from napari_simpleitk_image_processing import label_statistics

As an example image we will use an extracgt from the BBBC007 dataset image set version 1 (Jones et al., Proc. ICCV Workshop on Computer Vision for Biomedical Image Applications, 2005), available from the Broad Bioimage Benchmark Collection [Ljosa et al., Nature Methods, 2012].

blobs = imread('../../data/BBBC007_batch/17P1_POS0013_D_1UL.tif')
imshow(blobs)

Starting point: a segmented label image

labels = voronoi_otsu_labeling(blobs, spot_sigma=3.5)
imshow(labels, labels=True)

Label statistics

There is a function label_statistics that allows you to specify what you want to measure. Within the brackets behind, hit Shift+Tab to see what other options are available.

statistics = label_statistics(blobs, labels, size=True, shape=True, intensity=True)

df = pd.DataFrame(statistics)
df

	label	maximum	mean	median	minimum	sigma	sum	variance	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
0	1	189.0	95.594203	95.753906	35.0	38.840425	13192.0	1508.578652	1.797964	18.439089	1.797964	0.0	9.885624	17.773998	41.643236	6.627727	138	19
1	2	96.0	79.125000	80.871094	61.0	10.881669	633.0	118.410714	2.529822	4.000000	2.529822	0.0	2.006573	5.076274	10.026513	1.595769	8	5
2	3	138.0	83.482759	85.832031	35.0	28.606981	4842.0	818.359347	2.138700	12.041595	2.138700	0.0	5.876166	12.567359	26.997213	4.296740	58	13
3	4	164.0	93.620536	91.785156	35.0	33.346042	20971.0	1111.958500	1.654121	22.135944	1.654121	0.0	13.130927	21.720147	53.055320	8.444016	224	21
4	5	165.0	89.698795	90.792969	32.0	27.717207	22335.0	768.243587	1.232971	19.723083	1.232971	0.0	16.035346	19.771114	55.937700	8.902761	249	9
5	6	198.0	98.710801	94.761719	33.0	39.441423	28330.0	1555.625862	1.192244	20.615528	1.192244	0.0	17.507068	20.872698	60.054545	9.557978	287	0
6	7	83.0	63.454545	63.011719	49.0	11.201461	698.0	125.472727	3.675703	6.000000	3.675703	0.0	1.952006	7.174995	11.757129	1.871205	11	7
7	8	208.0	99.050179	94.761719	32.0	40.663972	27635.0	1653.558624	1.129494	19.723083	1.129494	0.0	17.734351	20.030834	59.211632	9.423824	279	0
8	9	192.0	101.791209	103.691406	31.0	36.220886	27789.0	1311.952569	1.280479	21.023796	1.280479	0.0	16.475937	21.097095	58.571488	9.321942	273	0
9	10	169.0	89.158088	86.824219	33.0	28.747161	24251.0	826.399270	1.367414	21.587033	1.367414	0.0	15.914372	21.761535	58.464116	9.304853	272	0
10	11	176.0	98.293233	98.730469	33.0	40.172315	13073.0	1613.814878	1.228297	13.601471	1.228297	0.0	11.741649	14.422238	40.881870	6.506552	133	0
11	12	233.0	100.278443	99.722656	33.0	41.033463	33493.0	1683.745059	1.259146	25.612497	1.259146	0.0	18.377658	23.140164	64.785552	10.310941	334	20
12	13	229.0	105.978992	107.660156	31.0	43.715270	25223.0	1911.024873	1.230081	20.000000	1.230081	0.0	15.695554	19.306806	54.688172	8.703893	238	13
13	14	194.0	97.106227	95.753906	33.0	34.742430	26510.0	1207.036468	1.088491	19.209373	1.088491	0.0	17.869976	19.451307	58.571488	9.321942	273	0
14	15	208.0	88.329897	85.832031	30.0	30.682935	25704.0	941.442517	1.155152	20.615528	1.155152	0.0	17.909437	20.688127	60.471595	9.624353	291	0
15	16	182.0	88.945652	82.855469	35.0	30.638332	16366.0	938.707413	1.330357	17.204651	1.330357	0.0	13.270272	17.654203	48.085468	7.653040	184	0
16	17	162.0	94.177778	96.746094	34.0	31.399563	21190.0	985.932540	1.248545	18.384776	1.248545	0.0	15.147614	18.912477	53.173616	8.462844	225	0
17	18	158.0	89.012121	85.832031	33.0	27.362737	14687.0	748.719364	1.067778	14.560220	1.067778	0.0	14.026736	14.977435	45.535164	7.247146	165	0
18	19	111.0	76.539568	74.917969	45.0	18.445873	10639.0	340.250235	1.176259	13.928388	1.176259	0.0	12.266227	14.428259	41.793845	6.651697	139	0
19	20	255.0	119.223301	113.613281	23.0	63.206889	24560.0	3995.110869	1.196578	17.088007	1.196578	0.0	14.805332	17.715736	50.878997	8.097644	206	0
20	21	185.0	97.503817	99.722656	29.0	42.506900	12773.0	1806.836524	1.188141	13.892444	1.188141	0.0	11.848319	14.077472	40.573323	6.457445	131	0
21	22	162.0	90.449275	90.792969	29.0	32.365628	12482.0	1047.533905	1.189990	14.212670	1.189990	0.0	12.151303	14.459935	41.643236	6.627727	138	0
22	23	107.0	61.803030	59.042969	37.0	14.898352	8158.0	221.960907	1.940889	17.088007	1.940889	0.0	9.305541	18.061026	40.727889	6.482045	132	0
23	24	166.0	88.323308	85.832031	31.0	37.578715	11747.0	1412.159831	1.266131	14.866069	1.266131	0.0	11.564891	14.642667	40.881870	6.506552	133	0
24	25	242.0	104.000000	97.738281	33.0	45.917939	29224.0	2108.457143	1.035791	19.646883	1.035791	0.0	18.585407	19.250605	59.423481	9.457541	281	0
25	26	126.0	76.512195	76.902344	41.0	20.510440	9411.0	420.678129	1.335736	14.317821	1.335736	0.0	10.827976	14.463318	39.314928	6.257165	123	0
26	27	198.0	103.388626	103.691406	33.0	37.321683	43630.0	1392.907994	1.025295	23.537205	1.025295	0.0	22.892162	23.471225	72.821758	11.589943	422	0
27	28	170.0	98.029536	98.730469	31.0	32.389630	23233.0	1049.088107	1.194861	19.104973	1.194861	0.0	15.891704	18.988383	54.573160	8.685588	237	0
28	29	216.0	99.525424	95.753906	34.0	38.262945	29360.0	1464.052923	1.017426	19.416488	1.017426	0.0	19.213862	19.548681	60.885789	9.690274	295	0
29	30	99.0	68.271739	69.957031	45.0	11.918315	6281.0	142.046226	1.459820	12.649111	1.459820	0.0	8.957757	13.076715	34.001560	5.411516	92	0
30	31	177.0	90.753676	90.792969	36.0	31.606582	24685.0	998.976001	1.028763	18.681542	1.028763	0.0	18.347713	18.875440	58.464116	9.304853	272	0
31	32	168.0	80.546980	74.917969	34.0	27.017132	24003.0	729.925395	1.333807	22.360680	1.333807	0.0	16.866176	22.496231	61.194595	9.739422	298	0
32	33	204.0	90.617021	86.824219	29.0	30.960820	29813.0	958.572392	1.118232	21.587033	1.118232	0.0	19.354730	21.643072	64.298802	10.233472	329	0
33	34	196.0	99.518272	95.753906	31.0	37.824029	29955.0	1430.657165	1.091743	20.615528	1.091743	0.0	18.736049	20.454958	61.501850	9.788323	301	0
34	35	124.0	78.697674	75.910156	37.0	23.695531	10152.0	561.478198	1.107833	13.152946	1.107833	0.0	12.176229	13.489225	40.262412	6.407962	129	0
35	36	182.0	76.945289	68.964844	34.0	30.643854	25315.0	939.045778	1.427831	26.400758	1.427831	0.0	17.128313	24.456337	64.298802	10.233472	329	0
36	37	168.0	84.812883	84.839844	29.0	31.099621	27649.0	967.186418	1.323957	24.515301	1.323957	0.0	17.706266	23.442328	64.004975	10.186708	326	0
37	38	177.0	100.131206	101.707031	29.0	36.379482	28237.0	1323.466710	1.302883	21.400935	1.302883	0.0	16.600718	21.628797	59.529123	9.474354	282	0
38	39	164.0	102.875676	105.675781	33.0	33.046337	38064.0	1092.060382	1.282351	26.305893	1.282351	0.0	19.166928	24.578724	68.187661	10.852403	370	0
39	40	233.0	94.528421	90.792969	33.0	38.361603	44901.0	1471.612587	1.657685	30.886890	1.657685	0.0	19.100769	31.663059	77.259472	12.296227	475	0
40	41	150.0	91.892308	95.753906	33.0	32.923978	11946.0	1083.988312	1.379006	14.764823	1.379006	0.0	10.955790	15.108098	40.418166	6.432751	130	0
41	42	236.0	101.597610	95.753906	30.0	43.330837	25501.0	1877.561434	1.263470	19.924859	1.263470	0.0	15.904114	20.094369	56.161900	8.938444	251	12
42	43	222.0	114.104895	110.636719	34.0	48.297889	16317.0	2332.686103	1.429075	16.278821	1.429075	0.0	11.287443	16.130602	42.390931	6.746726	143	17
43	44	162.0	87.120690	78.886719	34.0	38.912893	5053.0	1514.213249	2.408771	14.000000	2.408771	0.0	5.536959	13.337266	26.997213	4.296740	58	15

These are all columns that are available:

print(statistics.keys())

Index(['label', 'maximum', 'mean', 'median', 'minimum', 'sigma', 'sum',
       'variance', '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'],
      dtype='object')

To get an overview about the measurements, we can use the describe method.

df.describe().T

	count	mean	std	min	25%	50%	75%	max
label	44.0	22.500000	12.845233	1.000000	11.750000	22.500000	33.250000	44.000000
maximum	44.0	176.000000	40.704837	83.000000	162.000000	176.500000	199.500000	255.000000
mean	44.0	91.670929	11.991820	61.803030	86.543738	92.756422	99.520060	119.223301
median	44.0	90.116477	12.275056	59.042969	84.343750	91.289062	97.986328	113.613281
minimum	44.0	34.204545	6.121178	23.000000	31.000000	33.000000	35.000000	61.000000
sigma	44.0	33.064504	10.124795	10.881669	28.712116	32.985157	38.858542	63.206889
sum	44.0	20682.068182	10589.601489	633.000000	12348.000000	23618.000000	27684.000000	44901.000000
variance	44.0	1193.443088	682.363358	118.410714	824.389289	1088.024347	1509.987301	3995.110869
elongation	44.0	1.404297	0.488280	1.017426	1.170982	1.261308	1.391212	3.675703
feret_diameter	44.0	18.297767	5.102293	4.000000	14.499620	19.157173	21.118081	30.886890
flatness	44.0	1.404297	0.488280	1.017426	1.170982	1.261308	1.391212	3.675703
roundness	44.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
equivalent_ellipsoid_diameter_0	44.0	14.266726	4.654120	1.952006	11.697460	15.793629	17.768257	22.892162
equivalent_ellipsoid_diameter_1	44.0	18.317758	4.723680	5.076274	14.597830	19.119494	21.230020	31.663059
equivalent_spherical_perimeter	44.0	50.554583	14.457400	10.026513	40.881870	54.630666	60.158808	77.259472
equivalent_spherical_radius	44.0	8.046012	2.300967	1.595769	6.506552	8.694741	9.574572	12.296227
number_of_pixels	44.0	219.636364	104.901968	8.000000	133.000000	237.500000	288.000000	475.000000
number_of_pixels_on_border	44.0	3.431818	6.532202	0.000000	0.000000	0.000000	1.250000	21.000000