Skip to content
Related Articles

Related Articles

Improve Article

Mahotas – Re-Labeling

  • Last Updated : 22 Apr, 2021

In this article we will see how we can relabel the labelled image in mahotas. Relabeling is used to label the already labelled image, this is required because some times there are mane labels which user deletes so when that image get relabel, we get the new label number as well. We use mahotas.label method to label the image
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 
 

mhotas.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.relabel method 
 

Syntax : mahotas.relabel(labelled)
Argument : It takes labelled image object as argument
Return : It returns the labelled image and integer i.e number of labels 
 



Example 1: 
 

Python3




# importing required libraries
import mahotas
import numpy as np
from pylab import imshow, show
import os
 
# loading nuclear image
f = mahotas.demos.load('nuclear')
 
# setting filter to the image
f = f[:, :, 0]
 
# setting gaussian filter
f = mahotas.gaussian_filter(f, 4)
 
# setting threshold value
f = (f> f.mean())
 
# creating a labelled image
labelled, n_nucleus = mahotas.label(f)
 
# printing number of labels
print("Count : " + str(n_nucleus))
 
# showing the labelled image
print("Labelled Image")
imshow(labelled)
show()
 
# removing border labels
labelled = mh.labelled.remove_bordering(labelled)
 
# relabling the labelled image
relabelled, n_left = mahotas.labelled.relabel(labelled)
 
# showing number of labels
print("Count : " + str(n_left))
 
# showing the image
print("No border Label")
imshow(relabelled)
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())
 
# creating a labelled image
labelled, n_nucleus = mahotas.label(gaussian)
 
# printing number of labels
print("Count : " + str(n_nucleus))
  
print("Labelled Image")
# showing the gaussian filter
imshow(labelled)
show()
 
# removing border labels
labelled = mh.labelled.remove_bordering(labelled)
 
# relabling the labelled image
relabelled, n_left = mahotas.labelled.relabel(labelled)
 
# showing number of labels
print("Count : " + str(n_left))
 
# showing the image
print("No border Label")
imshow(relabelled)
show()

Output : 
 

 

 Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.  

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course




My Personal Notes arrow_drop_up
Recommended Articles
Page :