Mahotas - Haralick features

In this article we will see how we can get the haralick features of image in mahotas. Haralick texture features are calculated from a Gray Level Co-occurrence Matrix, (GLCM), a matrix that counts the co-occurrence of neighboring gray levels in the image. The GLCM is a square matrix that has the dimension of the number of gray levels N in the region of interest (ROI). For this we are going to use the fluorescent microscopy image from a nuclear segmentation benchmark. We can get the image with the help of command given below
 

mahotas.demos.nuclear_image()

Below is the nuclear_image 
 

👁 Image

In order to do this we will use mahotas.features.haralick method 
 

Syntax : mahotas.features.haralick(img)
Argument : It takes image object as argument
Return : It returns numpy.ndarray 
 

Note : The input of the this should  be the filtered image or 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]

Example 1 : 
 

Output : 
 

👁 Image

Example 2 : 
 

Output : 
 

👁 Image