使用Scikit-image进行统计分析

我们可以使用scikit-image从标签图像中提取特征。为了方便起见，我们使用napari-skimage-regionprops库。

在进行测量之前，我们需要一个image和相应的label_image。因此，我们回顾一下过滤、阈值处理和标记步骤：

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)

测量 / 区域属性

现在我们使用非常方便的函数regionprops_table。它基于scikit-image的regionprops 测量列表库提供特征。让我们首先检查一下这个函数需要我们提供什么：

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

我们看到需要提供我们想要测量的特征类别。这里展示了一种划分这些类别的方式：

默认情况下，设置为True的特征类别会被测量。在这种情况下，类别是size和intensity。但perimeter和shape也会很有趣去研究。所以我们也需要将它们设置为True。

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

如你所见，我们现在有很多特征可以研究。我们可以使用keys函数打印出所有特征名称：

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')

而describe给我们提供了每个特征的基本统计信息，如max、mean、min和std：

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

练习

制作一个只包含solidity、circularity和roundness的表格。