Mahotas - Skeletonization by thinning of image

In this article, we will see how we can do the skeletonization of images by thinning in mahotas. Skeletonization is a process for reducing foreground regions in a binary image to a skeletal remnant that largely preserves the extent and connectivity of the original region while throwing away most of the original foreground pixels. Thinning is a morphological operation that is used to remove selected foreground pixels from binary images, somewhat like erosion or opening.

In this tutorial, we will use the “Lena” image, below is the command to load it. 

mahotas.demos.load('lena')

Below is the Lena image

In order to do this we will use mahotas.thin method

Syntax : mahotas.thin(img) 

Argument : It takes image object as argument

Return : It returns image object 

Note: Input image should be filtered or should be loaded as grey

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]

Below is the implementation 

Output : 

Image threshold using Otsu Method

Skeletonised Image

Another example 

Output : 

Image threshold using Otsu Method

Skeletonised Image