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Mahotas – Highlighting Image Maxima

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  • Last Updated : 17 May, 2022
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In this article we will see how we can highlight the maxima of image in mahotas. Maxima can be best found in the distance map image because in labeled image each label is maxima but in distance map maxima can be identified easily. 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.morph.regmax method 
 

Syntax : mahotas.morph.regmax(img, Bc)
Argument : It takes image object and numpy ones array as argument
Return : It returns image object 
 

Note : The input of  this 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 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 image
nuclear = nuclear[:, :, 0]
 
# adding gaussian filter
nuclear = mahotas.gaussian_filter(nuclear, 4)
 
# setting threshold
threshed = (nuclear > nuclear.mean())
 
# creating distance map
dmap = mahotas.distance(threshed)
 
print("Distance Map")
# showing image
imshow(dmap)
show()
 
# numpy ones array
Bc = np.ones((3, 2))
 
# getting maxima
maxima = mahotas.morph.regmax(dmap, Bc = Bc)
 
# showing image
print("Maxima")
imshow(maxima)
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)
  
 
# getting distance map
dmap = mahotas.distance(labelled)
 
# showing image
print("Distance Map")
imshow(dmap)
show()
 
 
# numpy ones array
Bc = np.ones((4, 1))
 
# getting maxima
maxima = mahotas.morph.regmax(dmap, Bc = Bc)
 
# showing image
print("Maxima")
imshow(maxima)
show()

Output : 
 

 


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