This document discusses morphological image processing techniques. It begins by explaining that morphology uses mathematical morphology operations to extract image components and describe shapes. It then outlines common morphological algorithms like dilation, erosion, opening, closing, and hit-or-miss transformations. Dilation enlarges object boundaries while erosion shrinks them. Opening can smooth contours and closing can fuse breaks or fill gaps. These operations use a structuring element to transform images. The document provides examples of using morphological filters and algorithms for tasks like noise removal, region filling, and connected component extraction.

1. Morphological Image
Processing
Nandu Raj
Vinayak Narayanan

2. ‘Morphology’ - a branch of Biology which deals with
the form and structure of plants and animals.
Here, it is used as a tool for extracting image
components useful in describing image shape.
Programme chart
• Dilation and Erosion
• Opening and Closing
• Hit or Miss transformation
• Morph. algorithms

3. Dilation
In dilation, a small image called structuring element is used as a local
maximum operator. As the structuring element is scanned over the
image, we compute the maximal pixel value overlapped by B and
replace the image pixel under the anchor point with that maximal
value.
Structuring element B

4. Dilation contd…

5. Dilation contd...
Dilation gradually enlarges the boundaries of regions of foreground pixels.
Thus areas of foreground regions grow in size while holes within those
regions become smaller.

6. Dilated grayscale image

7. Erosion
Erosion is the converse of dilation. The action of the erosion operator
is equivalent to computing a local minimum over the area of the
kernel. As the kernel is scanned over the image, we compute the
minimal pixel value overlapped by B and replace the image pixel
under the anchor point with that minimal value.

8. Erosion contd…

9. Erosion contd…
Erosion is the converse of dilation. The action of the erosion operator
is equivalent to computing a local minimum over the area of the
kernel. As the kernel is scanned over the image, we compute the
minimal pixel value overlapped by B and replace the image pixel
under the anchor point with that minimal value.

10. Eroded grayscale image

11. Opening
Opening generally smoothens the contour of an object, breaks narrow
isthmuses, and eliminates thin protrusions.
The opening of set A by structuring element B, denoted A ◦ B, is defined as,

12. Opening – geometrical interpretation
Suppose that we view the structuring element B as a (flat) "rolling ball."
The boundary of A ◦ B is then established by the points in B that reach the
farthest into the boundary of A as B is rolled around the inside of this
boundary.

13. Opening – step by step

14. Closing
Closing also tends to smooth sections of contours but, as opposed to
opening, it generally fuses narrow breaks and long thin gulfs, eliminates small
holes, and fills gaps in the contour.
The closing of set A by structuring element B, denoted A • B, is
defined as,

15. Closing – geometrical interpretation
Closing has a similar geometric interpretation, except that now we roll B on
the outside of the boundary.

16. Closing – step by step

17. A morphological filter
We have a binary image showing a section of a fingerprint corrupted
by noise. The noise manifests itself as light elements on a dark
background and as dark elements on the light components of the
fingerprint. The objective is to eliminate the noise and its effects on
the print while distorting it as little as possible. A morphological filter
consisting of opening followed by closing can be used to accomplish
this objective.
Noisy image Structuring element

18. A morphological filter
Noisy image Eroded image
Opening Dilation of opening
Closing

19. The Hit-or-Miss Transformation
Basic tool for shape detection.
Our aim is to find the center of gravity of X in the image. Here dark is “1”.

20. The Hit-or-Miss Transformation

21. Some morphological algorithms
1. Boundary Extraction

23. Dilation-Recap

24. 2. Region Filling (Conditional Dilation)
The algorithm terminates at step ‘k’ if Xk=Xk-1

26. Now, these two are the
same. Hence, the
algorithm ends.
The final step is to
perform its union with A.

27. 3. Extraction of connected components

29. Thank You