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 

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 :

Python3

# importing various libraries
import mahotas
import mahotas.demos
import mahotas as mh
import numpy as np
from pylab import imshow, show

# loading nuclear image
nuclear = mahotas.demos.nuclear_image()

# filtering image
nuclear = nuclear[:, :, 0]

# adding gaussian filter
nuclear = mahotas.gaussian_filter(nuclear, 4)

# setting threshold
threshed = (nuclear > nuclear.mean())

# making is labeled image
labeled, n = mahotas.label(threshed)

# showing image
print("Labelled Image")
imshow(labeled)
show()

# getting haralick features
h_feature = mahotas.features.haralick(labelled)

# showing the feature
print("Haralick Features")
imshow(h_feature)
show()

Output :

Example 2 :

Python3

# importing required libraries
import numpy as np
import mahotas
from pylab import imshow, show

# loading image
img = mahotas.imread('dog_image.png')

# filtering the image
img = img[:, :, 0]

# setting gaussian filter
gaussian = mahotas.gaussian_filter(img, 15)

# setting threshold value
gaussian = (gaussian > gaussian.mean())

# making is labelled image
labeled, n = mahotas.label(gaussian)

# showing image
print("Labelled Image")
imshow(labelled)
show()

# getting haralick features
h_feature = mahotas.features.haralick(labelled)

# showing the feature
print("Haralick Features")
imshow(h_feature)
show()

Output :