In this article we will see how we can get the perimeter of objects in binary images 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
A pixel is part of an object perimeter if its value is one and there is at least one zero-valued pixel in its neighborhood. By default the neighborhood of a pixel is 4 nearest pixels, but if we can set it to 8 then 8 nearest pixels will be considered.
In order to do this we will use mahotas.labelled.bwperim method
Syntax : mahotas.labeled.bwperim(image, n)
Argument : It takes numpy.ndarray object i.e labelled image preferred black and white and integer i.e nearest pixels which is optional
Return : It returns numpy.ndarray object i.e a boolean image
Note : The input of the this should be the filtered image object which is labelled
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 required librariesimport mahotasimport numpy as npfrom pylab import imshow, showimport os# loading nuclear imagef = mahotas.demos.load('nuclear')# setting filter to the imagef = f[:, :, 0]# setting gaussian filterf = mahotas.gaussian_filter(f, 4)# setting threshold valuef = (f> f.mean())# creating a labelled imagelabelled, n_nucleus = mahotas.label(f)# showing the labelled imageprint("Labelled Image")imshow(labelled)show()# getting perimetersrelabelled = mahotas.labelled.bwperim(labelled)# showing the imageprint("Perimeters Image")imshow(relabelled)show() |
Output :
Example 2 :
Python3
# importing required librariesimport numpy as npimport mahotasfrom pylab import imshow, show # loading imageimg = mahotas.imread('dog_image.png') # filtering the imageimg = img[:, :, 0] # setting gaussian filtergaussian = mahotas.gaussian_filter(img, 15) # setting threshold valuegaussian = (gaussian > gaussian.mean()) # creating a labelled imagelabelled, n_nucleus = mahotas.label(gaussian) print("Labelled Image")# showing the gaussian filterimshow(labelled)show() # getting perimetersrelabeled = mahotas.labelled.bwperim(labelled, 8)# showing the imageprint("Perimeters Image")imshow(relabelled)show() |
Output :
