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 labeled 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
Labeled 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_labeled method
Syntax : mahotas.label.filter_labeled(label_image, filter1, filter2)
Argument : It takes labeled 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:
# 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 labeled image labeled, n_nucleus = mahotas.label(f) # printing number of labels print("Count : " + str(n_nucleus)) # showing the labeleed image print("Labelled Image") imshow(labeled) show() # filtering the label image # adding border filter relabeled, n_left = mahotas.labeled.filter_labeled(labeled, remove_bordering = True) # showing number of labels print("Count : " + str(n_left)) # showing the image print("No border Label") imshow(relabeled) show() |
Output :
Example 2:
# 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 labeled image labeled, n_nucleus = mahotas.label(f) # printing number of labels print("Count : " + str(n_nucleus)) # showing the labeleed image print("Labelled Image") imshow(labeled) show() # filtering the label image # adding max size filter relabeled, n_left = mahotas.labeled.filter_labeled(labeled, max_size = 7000) # showing number of labels print("Count : " + str(n_left)) # showing the image print("Max size 7000 Label") imshow(relabeled) show() |
Output :
Recommended Posts:
- Mahotas - Getting Border of labels
- Mahotas - Filtering Region
- Mahotas - Gaussian filtering
- PyQt5 - lower() method for Labels
- wxPython - Change labels using button
- Setting the position of TKinter labels
- Multiple Labels Using Convolutional Neural Networks
- Tableau - Joining data files with inconsistent labels
- How to drop rows in Pandas DataFrame by index labels?
- NLP | Filtering Insignificant Words
- Python | Bilateral Filtering
- Spatial Filtering and its Types
- Python - Filtering text using Enchant
- PyQt5 QColorDialog - Setting border to the child labels present inside it
- Python - Sharpen and blur filtering using pgmagick
- NLP | Training a tokenizer and filtering stopwords in a sentence
- Python | Filtering data with Pandas .query() method
- Filtering Images based on size attributes in Python
- Multi-Label Image Classification - Prediction of image labels
- Mahotas - Mean filter
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.
