Datasets with 3 or more spatial dimensions¶
Most scikit-image functions are compatible with 3D datasets, i.e., images with
3 spatial dimensions (to be distinguished from 2D multichannel images, which
are also arrays with
skimage.data.cells3d() returns a 3D fluorescence microscopy
image of cells. The returned dataset is a 3D multichannel image with dimensions
(z, c, y, x) order. Channel 0 contains cell membranes, while channel
1 contains nuclei.
The example below shows how to explore this dataset. This 3D image can be used to test the various functions of scikit-image.
from skimage import data import plotly import plotly.express as px img = data.cells3d()[20:] fig = px.imshow(img, facet_col=1, animation_frame=0, binary_string=True, binary_format='jpg') fig.layout.annotations['text'] = 'Cell membranes' fig.layout.annotations['text'] = 'Nuclei' plotly.io.show(fig)
Total running time of the script: ( 0 minutes 2.995 seconds)