In the previous blogs, we discussed various thresholding algorithms like otsu, adaptive, BHT, etc. All these resulted in a binary image which in general are distorted by noise, holes, etc. Thus there is a need to process these images so as to remove the imperfections. And sometimes, we also need to extract image features such as boundaries, etc that are useful in the representation of the object of interest. This all is done using Morphological image processing that applies non-linear transformations based on the image shape. Now, let’s discuss why the name morphology?

In general, morphology stands for the study of the form and the structure of the things. Because as a result of conversion to the binary image, we have lost the intensity information. Thus the only information that remains is the spatial location or the structure of the image. That’s why known as morphological image processing.

Morphological image processing was originally developed for binary images but later this was also extended to the grayscale images. The watershed algorithm is an outcome of this generalization.

Let’s understand the concept behind the morphological image processing (MIP) wrt. convolution operation that we studied earlier.

Remember in convolution, we have a filter/window and we move this filter over the image. The output values are then computed by some linear operation between the filter weights and the pixel values. Similarly, in MIP we have a structuring element and we move this over the entire image. The output values are computed by applying non-linear operations like set operations (intersection, union, etc.) between the structuring element and the underlying pixel values. These non-linear operations are known as morphological operations.

So, the above paragraph contains two terms – structuring element and the morphological operations. So, let’s understand what’s a structuring element?

The structuring element is a binary image (consisting of 0’s and 1’s) that is used to probe an image for finding the region of interest. For instance, if we want to detect lines in an image, we create a linear structuring element. The pattern of 1’s and 0’s specifies the shape of the structuring element. Below is an example of elliptical SE.

Mostly the dimensions of the SE are odd with the origin at the center. OpenCV provides a builtin function for creating SE as shown below.

Here, shape refers to the SE shape. This can take one of the following values

• cv2.MORPH_RECT – creates a rectangular SE.
• cv2.MORPH_ELLIPSE – creates an elliptical SE.
• cv2.MORPH_CROSS – cross-shaped SE.

The “ksize” specifies the size of the SE and anchor specifies the origin position (default is (-1,-1) i.e at the center of the SE). Below is an example that creates an elliptical SE.

To obtain the output, morphological operations are performed between the SE and the underlying pixel values. Morphological operations are nothing but basic set operations like union, intersection, etc. For instance, an example of morphological operations can be the set of all values such that at least one of the SE pixel values is equal to the underlying image pixel values. This operation usually leads to an increase in the size of the object and fills the holes if present in the object. Below figure shows this morphological operation.

If you use other SE, the result would be different. So, select the shape of the SE according to your application. In the next blog, we will discuss various morphological operations in detail. Hope you enjoy reading.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.