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Mahotas – Filtering Labels

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In this article we will see how we can filter the labels of the labelled image in mahotas. Filtering label is similar to implementing the relabeling feature but the difference is in filtering we will remove i.e filter the labels at the time of calling the filtering method and filtering will give us new labelled image and number of labels. 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 
 

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.filter_labelled method 
 

Syntax : mahotas.label.filter_labelled(label_image, filter1, filter2)
Argument : It takes labelled image object and filters as argument
Return : It returns the labelled image and integer i.e number of labels 
 


Note : Filters can be border label filter, maximum size anything.
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()
 
 
# filtering the label image
# adding border filter
relabelled, n_left = mahotas.labelled.filter_labelled(labelled, remove_bordering = True)
 
# showing number of labels
print("Count : " + str(n_left))
 
# showing the image
print("No border Label")
imshow(relabelled)
show()

                    

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()
 
 
# filtering the label image
# adding border filter
relabelled, n_left = mahotas.labelled.filter_labelled(labelled, remove_bordering = True)
 
# showing number of labels
print("Count : " + str(n_left))
 
# showing the image
print("No border Label")
imshow(relabelled)
show()

                    

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()
 
 
# filtering the label image
# adding border filter
relabelled, n_left = mahotas.labelled.filter_labelled(labelled, remove_bordering = True)
 
# 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 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()
 
 
# filtering the label image
# adding max size filter
relabelled, n_left = mahotas.labelled.filter_labelled(labelled, max_size = 7000)
 
# showing number of labels
print("Count : " + str(n_left))
 
# showing the image
print("Max size 7000 Label")
imshow(relabelled)
show()

                    

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()
 
 
# filtering the label image
# adding max size filter
relabelled, n_left = mahotas.labelled.filter_labelled(labelled, max_size = 7000)
 
# showing number of labels
print("Count : " + str(n_left))
 
# showing the image
print("Max size 7000 Label")
imshow(relabelled)
show()

                    

Output : 
 


 



Last Updated : 24 Feb, 2023
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