In this article we will see how we can create a labelled image from the normal image in mahotas. 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
Labelled images are integer images where the values correspond to different regions. I.e., region 1 is all of the pixels which have value 1, region two is the pixels with value 2, and so on
In order to do this we will use mahotas.label method
Syntax : mahotas.label(image)
Argument : It takes loaded image object as argument
Return : It returns the labelled image and the integer i.e number of labels
Note : The input of the label should be the filtered image object and it should have the threshold and it is preferred that image should have gaussian filter for removing sharper edges.
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 required librariesimport mahotasimport numpy as npfrom pylab import imshow, showimport os# loading nuclear imagef = mahotas.demos.load('nuclear')# setting filter to the imagef = f[:, :, 0]# show the imageprint("Image")imshow(f)show()# setting gaussian filterf = mahotas.gaussian_filter(f, 4)# setting threshold valuef = (f> f.mean())# creating a labelled imagelabelled, n_nucleus = mahotas.label(f)# showing the labelled imageprint("Labelled Image")imshow(labelled)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] print("Image")# showing the imageimshow(img)show() # setting gaussian filtergaussian = mahotas.gaussian_filter(img, 15)# setting threshold valuegaussian = (gaussian > gaussian.mean())# creating a labelled imagelabelled, n_nucleus = mh.label(gaussian) print("Labelled Image")# showing the gaussian filterimshow(labelled)show() |
Output :
