Mahotas – Filtering Labels
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
mhotas.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 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)# printing number of labelsprint("Count : " + str(n_nucleus))# showing the labelled imageprint("Labelled Image")imshow(labelled)show()# filtering the label image# adding border filterrelabelled, n_left = mahotas.labelled.filter_labelled(labelled, remove_bordering = True)# showing number of labelsprint("Count : " + str(n_left))# showing the imageprint("No border Label")imshow(relabelled)show() |
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)# printing number of labelsprint("Count : " + str(n_nucleus))# showing the labelled imageprint("Labelled Image")imshow(labelled)show()# filtering the label image# adding border filterrelabelled, n_left = mahotas.labelled.filter_labelled(labelled, remove_bordering = True)# showing number of labelsprint("Count : " + str(n_left))# showing the imageprint("No border Label")imshow(relabelled)show() |
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)# printing number of labelsprint("Count : " + str(n_nucleus))# showing the labelled imageprint("Labelled Image")imshow(labelled)show()# filtering the label image# adding border filterrelabelled, n_left = mahotas.labelled.filter_labelled(labelled, remove_bordering = True)# showing number of labelsprint("Count : " + str(n_left))# showing the imageprint("No border Label")imshow(relabelled)show() |
Output :
Example 2:
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)# printing number of labelsprint("Count : " + str(n_nucleus))# showing the labelled imageprint("Labelled Image")imshow(labelled)show()# filtering the label image# adding max size filterrelabelled, n_left = mahotas.labelled.filter_labelled(labelled, max_size = 7000)# showing number of labelsprint("Count : " + str(n_left))# showing the imageprint("Max size 7000 Label")imshow(relabelled)show() |
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)# printing number of labelsprint("Count : " + str(n_nucleus))# showing the labelled imageprint("Labelled Image")imshow(labelled)show()# filtering the label image# adding max size filterrelabelled, n_left = mahotas.labelled.filter_labelled(labelled, max_size = 7000)# showing number of labelsprint("Count : " + str(n_left))# showing the imageprint("Max size 7000 Label")imshow(relabelled)show() |
Output :
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