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 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
iamge = image[:, :, 0]
Example 1 :
# 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 iamge 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 iamge print("Labelled Image") imshow(labeled) show() # getting haralick features h_feature = mahotas.features.haralick(labeled) # showing the feature print("Haralick Features") imshow(h_feature) show() |
Output :
Example 2 :
# importing required libraries import numpy as np import mahotas from pylab import imshow, show # loading iamge img = mahotas.imread('dog_image.png') # filtering the imagwe img = img[:, :, 0] # setting gaussian filter gaussian = mahotas.gaussian_filter(img, 15) # setting threshold value gaussian = (gaussian > gaussian.mean()) # making is labeled image labeled, n = mahotas.label(gaussian) # showing iamge print("Labelled Image") imshow(labeled) show() # getting haralick features h_feature = mahotas.features.haralick(labeled) # showing the feature print("Haralick Features") imshow(h_feature) show() |
Output :
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