Extract texture features
This example shows how to extract texture features from the tissue image.
Textures features give give a measure of how the image intensity at different distances and angles varies by
calculating a grey-level co-occurrence matrix (GLCM).
The GLCM includes the number of times that grey-level \(j\) occurs at a distance \(d\)
and at an angle \(\\theta\) from grey-level \(i\).
From this data, different features (props) are calculated.
See also skimage.feature.greycomatrix().
Texture features are calculated by using features = 'texture', which calls
squidpy.im.ImageContainer.features_texture().
In addition to feature_name and channels, we can also specify the following features_kwargs:
distances- distances that are taken into account for finding repeating patterns.
angles- range on which values are binned. Default is the whole image range.
props- texture features that are extracted from the GLCM.
See also
See Extract image features for general usage of
squidpy.im.calculate_image_features().
import scanpy as sc
import squidpy as sq
Let’s load the fluorescence Visium dataset and calculate texture features with default features_kwargs.
Note that for texture features it may make sense to compute them over a larger crop size to include more context,
e.g., spot_scale = 2 or spit_scale = 4 which will extract crops with double or four times the radius
than the original Visium spot size.
For more details on the image cropping, see Crop images with ImageContainer.
# get spatial dataset including high-resolution tissue image
img = sq.datasets.visium_fluo_image_crop()
adata = sq.datasets.visium_fluo_adata_crop()
# calculate texture features and save in key "texture_features"
sq.im.calculate_image_features(
adata,
img,
features="texture",
key_added="texture_features",
spot_scale=2,
show_progress_bar=False,
)
The result is stored in adata.obsm['texture_features'].
adata.obsm["texture_features"].head()
Use squidpy.pl.extract() to plot the texture features on the tissue image or have a look at
our interactive visualization tutorial to learn
how to use our interactive napari plugin.
Here, we show the contrast feature for channels 0 and 1.
The two stains, DAPI in channel 0, and GFAP in channel 1 show different regions of high contrast.
sc.pl.spatial(
sq.pl.extract(adata, "texture_features"),
color=[None, "texture_ch-0_contrast_dist-1_angle-0.00", "texture_ch-1_contrast_dist-1_angle-0.00"],
bw=True,
)
Total running time of the script: ( 2 minutes 38.978 seconds)
Estimated memory usage: 332 MB