Mahotas – Labelled Image from the Normal Image
Last Updated :
24 Feb, 2023
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
import mahotas
import numpy as np
from pylab import imshow, show
import os
f = mahotas.demos.load('nuclear')
f = f[:, :, 0]
print("Image")
imshow(f)
show()
f = mahotas.gaussian_filter(f, 4)
f = (f> f.mean())
labelled, n_nucleus = mahotas.label(f)
print("Labelled Image")
imshow(labelled)
show()
|
Output :
Example 2 :
Python3
import numpy as np
import mahotas
from pylab import imshow, show
img = mahotas.imread('dog_image.png')
img = img[:, :, 0]
print("Image")
imshow(img)
show()
gaussian = mahotas.gaussian_filter(img, 15)
gaussian = (gaussian > gaussian.mean())
labelled, n_nucleus = mh.label(gaussian)
print("Labelled Image")
imshow(labelled)
show()
|
Output :
Like Article
Suggest improvement
Share your thoughts in the comments
Please Login to comment...