Mahotas – Setting Threshold

In this article we will see how we can set threshold to the 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()

Image thresholding is a simple form of image segmentation. It is a way to create a binary image from a grayscale or full-color image. This is typically done in order to separate “object” or foreground pixels from background pixels to aid in image processing.
Below is the nuclear_image 

In order to set threshold to the image we will take the image object which is numpy.ndarray and will divide the array with the threshold value, here threshold value is the means value, below is the command to do this 

img = (img < img.mean())]

Example 1 :

Python3

# importing required libraries
import mahotas as mh
import mahotas.demos
import numpy as np
from pylab import imshow, show

# getting nuclear image
nuclear = mh.demos.nuclear_image()


# filtering the image
nuclear = nuclear[:, :, 0]

print("Image with filter")
# showing the image
imshow(nuclear)
show()

# setting image threshold
nuclear = (nuclear < nuclear.mean())

print("Image with threshold")
# showing the threshold image
imshow(nuclear)
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]

print("Image with filter")
# showing the image
imshow(img)
show()

# setting threshold
img = (img < img.mean())

print("Image with Threshold")
# showing the threshold image
imshow(img)
show()

Output :