Regional properties of label
In this notebook we charcterize labels according to the mean and standard deviation of their propteries, such as size. If similarly sized objects are neighbors, the standard deviation of their size is low. If labels of different size are neighbors the standard deviation of their size is higher. This could be used to identify regions in tissues where cells of different size meet.
The data we use here was derived from maize_clsm.tif was taken from here, an image shared by David Legland under CCBY 4.0 license

shape  (640, 639) 
dtype  uint8 
size  399.4 kB 
min  0  max  255 

A corresponding label image looks like this:

cle._ image
shape  (640, 639) 
dtype  uint32 
size  1.6 MB 
min  1.0  max  254.0 

Measuring size
First we need to quantify size of the objects. We can immediately visualize these measurements as parametric image.

cle._ image
shape  (640, 639) 
dtype  float32 
size  1.6 MB 
min  201.0  max  14283.0 
