Mahotas – Re-Labeling
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 many 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
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.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) # relabeling 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) # relabeling 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 :