Mahotas - Threshold Adjacency Statistics

In this article, we will see how we can get the image's threshold adjacency statistics feature in mahotas. TAS was presented by Hamilton et al. in “Fast automated cell phenotype image classification”. TAS gives original parameters, unlike PFTAS which gives a variation without any hardcoded parameters.
For this tutorial, we will use the 'Lena' image, below is the command to load the Lena image 
 

mahotas.demos.load('lena')

Below is the Lena image 
 

In order to do this we will use mahotas.features.tas method
Syntax : mahotas.features.tas(img)
Argument : It takes image object as argument
Return : It returns 1-D array 
 

Note: Input image should be filtered or should be loaded as grey
In order to filter the image we will take the image object which is numpy.ndarray and filter it with the help of indexing, below is the command to do this
 

image = image[:, :, 0]

Below is the implementation 
 

Output :
 

Image

[8.18235887e-01 4.96278071e-02 3.85778412e-02 5.42293510e-02
 2.31141496e-02 8.96518478e-03 4.17582280e-03 2.30390223e-03
 7.70054279e-04 8.11830699e-01 5.42434618e-02 3.79106870e-02
 5.78859183e-02 2.54097764e-02 7.40147155e-03 2.98681431e-03
 1.76294893e-03 5.68223210e-04 8.69779571e-01 3.56911714e-02
 2.61354551e-02 4.12780295e-02 1.73316328e-02 5.09194046e-03
 2.56976434e-03 1.52282331e-03 5.99611680e-04 7.43348142e-01
 5.80286091e-02 4.97388078e-02 7.46472685e-02 3.83537568e-02
 1.81614021e-02 1.17267978e-02 4.57940731e-03 1.41580823e-03
 9.37920200e-01 1.55393289e-02 1.20666222e-02 1.87743206e-02
 9.61712375e-03 3.05412151e-03 1.93789436e-03 8.37170364e-04
 2.53218197e-04 9.13099391e-01 2.42303089e-02 1.70045074e-02
 2.72925208e-02 1.13702921e-02 3.81980697e-03 1.62341796e-03
 1.19050651e-03 3.69248007e-04]

Another example 
 

Output :
 

Image

[8.92356868e-01 2.75272814e-02 2.05523535e-02 3.43358813e-02
 1.80176597e-02 5.01153448e-03 1.33785553e-03 6.79775240e-04
 1.80791287e-04 8.81674218e-01 3.13932157e-02 2.34006832e-02
 3.69160363e-02 1.95048908e-02 5.11444295e-03 1.23809709e-03
 6.09325269e-04 1.49090226e-04 9.06137850e-01 2.75823883e-02
 2.03761048e-02 2.88661485e-02 1.36743022e-02 2.68646310e-03
 4.75770564e-04 1.39449993e-04 6.15220557e-05 8.35720148e-01
 4.69532212e-02 3.62894953e-02 5.08719737e-02 2.36920394e-02
 4.84714813e-03 1.21050472e-03 2.87238408e-04 1.28231432e-04
 9.38717680e-01 1.80549908e-02 1.33994005e-02 1.87263793e-02
 8.80054720e-03 1.75569656e-03 3.62486722e-04 1.35538513e-04
 4.72808768e-05 9.05435494e-01 2.48433294e-02 1.91342383e-02
 2.97531477e-02 1.52476648e-02 4.03149662e-03 1.02763639e-03
 4.30377634e-04 9.66153873e-05]