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
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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 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 librariesimport mahotasimport mahotas.demosimport mahotas as mhimport numpy as npfrom pylab import imshow, show# loading nuclear imagenuclear = mahotas.demos.nuclear_image()# filtering imagenuclear = nuclear[:, :, 0]# adding gaussian filternuclear = mahotas.gaussian_filter(nuclear, 4)# setting thresholdthreshed = (nuclear > nuclear.mean())# making is labeled imagelabeled, n = mahotas.label(threshed)# showing imageprint("Labelled Image")imshow(labeled)show()# getting haralick featuresh_feature = mahotas.features.haralick(labelled)# showing the featureprint("Haralick Features")imshow(h_feature)show() |
Output :
Example 2 :
Python3
# importing required librariesimport numpy as npimport mahotasfrom pylab import imshow, show # loading imageimg = mahotas.imread('dog_image.png') # filtering the imageimg = img[:, :, 0] # setting gaussian filtergaussian = mahotas.gaussian_filter(img, 15) # setting threshold valuegaussian = (gaussian > gaussian.mean()) # making is labelled imagelabeled, n = mahotas.label(gaussian)# showing imageprint("Labelled Image")imshow(labelled)show()# getting haralick featuresh_feature = mahotas.features.haralick(labelled)# showing the featureprint("Haralick Features")imshow(h_feature)show() |
Output :
