What is morphological image processing?

Morphological image processing (MIP) is a group of non-linear operations that correlate to the shape or morphology of features in an image. The morphological operations depend only on the comparative arrangement of pixel values, not their numerical values. They are particularly suitable for processing binary images.

Grayscale images also benefit from morphological operations because their optical transfer functions are unknown, and their absolute pixel values are of little or no interest.

The structuring element

A structuring element is a small shape or template that morphological techniques use to examine an image. These techniques examine all possible locations of the structuring element in the image, and compared them to the corresponding pixel neighborhood.

1. Fit: When all of the structuring element's pixels completely encircle the target object's pixels

2. Hit: When at least one structural element's pixels overlap an object's pixels

3. Miss: When the object's pixels are not covered by any pixels in the structuring element

Some tests see if an object "fits" into its surroundings, while others look for instances where it "hits" or intersects the surroundings.

Types of morphological operations

The structuring element's size and shape help us determine how many pixels are added to or subtracted from the object in the image. We define the morphological operation as dilatation or erosion by the rule that processes the pixels.

• Dilation: In the dilation process, we add pixels to the bounded objects.

• Erosion: In the erosion process, we remove pixels from the edges of objects.

Dilation

The highest value of all the pixels in the vicinity makes up the output pixel's value. A pixel in a binary image is set to 1 if adjacent pixels have a value of 1.

Morphological dilation fills up small gaps in objects and increases object visibility. Shapes with filling appear more prominent, and lines appear thicker.

Some important properties of dilation are as follows:

• It can mend damages.

• It can correct encroachments.

Erosion

The lowest value of all the neighborhood pixels makes up the output pixel's value. A pixel in a binary image is set to 0 if its neighbors also have a value of 0.

Floating pixels and thin lines are eliminated through morphological erosion, leaving only substantial objects.

Some important properties of erosion are as follows:

• It can disassemble joint objects.

• It can remove extrusions.

Use cases

A few use cases of morphological image processing are as follows:

• Dilation broadens items and fills in tiny gaps in the object.

• Small items are eliminated by erosion, leaving only actual objects.

• We can find the perimeter of objects in the binary image.

• It reduces all objects to lines in a 2-D binary image or 3-D binary volume.

• It helps us suppress light structures connected to the image border.