%matplotlib inline

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 {doc}`compute_features` for general usage
of {func}`squidpy.im.calculate_image_features`.

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()

	texture_ch-0_contrast_dist-1_angle-0.00	texture_ch-0_contrast_dist-1_angle-0.79	texture_ch-0_contrast_dist-1_angle-1.57	texture_ch-0_contrast_dist-1_angle-2.36	texture_ch-0_dissimilarity_dist-1_angle-0.00	texture_ch-0_dissimilarity_dist-1_angle-0.79	texture_ch-0_dissimilarity_dist-1_angle-1.57	texture_ch-0_dissimilarity_dist-1_angle-2.36	texture_ch-0_homogeneity_dist-1_angle-0.00	texture_ch-0_homogeneity_dist-1_angle-0.79	...	texture_ch-2_homogeneity_dist-1_angle-1.57	texture_ch-2_homogeneity_dist-1_angle-2.36	texture_ch-2_correlation_dist-1_angle-0.00	texture_ch-2_correlation_dist-1_angle-0.79	texture_ch-2_correlation_dist-1_angle-1.57	texture_ch-2_correlation_dist-1_angle-2.36	texture_ch-2_ASM_dist-1_angle-0.00	texture_ch-2_ASM_dist-1_angle-0.79	texture_ch-2_ASM_dist-1_angle-1.57	texture_ch-2_ASM_dist-1_angle-2.36
AAACGAGACGGTTGAT-1	42.783204	79.464035	41.904014	82.624826	1.983783	2.753093	1.973759	2.743151	0.753973	0.725217	...	0.570987	0.504941	0.883396	0.787901	0.872758	0.790485	0.040632	0.035577	0.041006	0.035397
AAAGGGATGTAGCAAG-1	82.756940	144.883230	76.546612	159.714604	3.349644	4.369327	3.171514	4.603538	0.692667	0.666414	...	0.538010	0.466651	0.938821	0.914061	0.947862	0.887927	0.016620	0.013672	0.016786	0.013555
AAATGGCATGTCTTGT-1	27.093979	48.276535	23.560334	49.362415	2.416785	3.209199	2.249740	3.271754	0.565910	0.525931	...	0.571515	0.503682	0.878716	0.781444	0.873200	0.786576	0.033804	0.028822	0.034247	0.028759
AAATGGTCAATGTGCC-1	24.198313	36.550901	18.040215	46.083141	2.222673	2.732854	1.925904	3.103483	0.645956	0.621034	...	0.578051	0.511233	0.988060	0.979815	0.987566	0.979121	0.016216	0.013678	0.016297	0.013659
AAATTAACGGGTAGCT-1	21.413928	39.826111	23.691475	47.908006	1.281552	1.779400	1.349581	1.883277	0.821503	0.798561	...	0.577136	0.507679	0.954380	0.926691	0.954430	0.922658	0.026097	0.022120	0.026564	0.022041

5 rows × 60 columns

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.

sq.pl.spatial_scatter(
    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",
    ],
    img_cmap="gray",
)