0. DIGITAL IMAGE PROCESSING
Chapter 9 – Morphological Image Processing
J. Shanbehzadeh S.S.Nobakht
Khwarizmi University of Tehran

1. Table of Contents 9.5 Some Basic Morphological Algorithm
9.5.1 Boundary Extraction
9.5.2 Hole Filling
9.5.3 Extraction of Connected Components
9.5.4 Convex Hull
9.5.5 Thinning
9.5.6 Thickening
9.5.7 Skeletons
9.5.8 Pruning
9.5.9 Morphological Reconstruction
Summary of Morphological Operations on Binary Images

2. Erosion 2
(B)z = {c | c = b + z, for b є B}
Erosion

3. Boundary Extraction

4. Boundary Extraction

5. Table of Contents 9.5 Some Basic Morphological Algorithm
9.5.1 Boundary Extraction
9.5.2 Hole Filling
9.5.3 Extraction of Connected Components
9.5.4 Convex Hull
9.5.5 Thinning
9.5.6 Thickening
9.5.7 Skeletons
9.5.8 Pruning
9.5.9 Morphological Reconstruction
Summary of Morphological Operations on Binary Images

6. Dilation 6
(B)z = {c | c = b + z, for b є B}
Dilation

7. Hole Filling

8. Hole Filling

9. Table of Contents 9.5 Some Basic Morphological Algorithm
9.5.1 Boundary Extraction
9.5.2 Hole Filling
9.5.3 Extraction of Connected Components
9.5.4 Convex Hull
9.5.5 Thinning
9.5.6 Thickening
9.5.7 Skeletons
9.5.8 Pruning
9.5.9 Morphological Reconstruction
Summary of Morphological Operations on Binary Images

10. Extraction of Connected Components

11. Extraction of Connected Components
تصویر سینه مرغ حاوی استخوان هایی است. تصویر اشعه ایکس است.
ابتدا تصویر را باینری کرده ایم. سپس با استفاده از Connected Component عناصر با سایز کوچکتر از 5*5 را حذف کرده ایم. چون عناصر بزرگتر از این سایز برای ما اهمیت دارند.

12. Table of Contents 9.5 Some Basic Morphological Algorithm
9.5.1 Boundary Extraction
9.5.2 Hole Filling
9.5.3 Extraction of Connected Components
9.5.4 Convex Hull
9.5.5 Thinning
9.5.6 Thickening
9.5.7 Skeletons
9.5.8 Pruning
9.5.9 Morphological Reconstruction
Summary of Morphological Operations on Binary Images

13. The Hit-or-Miss Transformation
Convex Hull
Closing
Opening
Dilation
Erosion
The Hit-or-Miss Transformation

14. Convex Hull Convex Concave
تفاوت پوسته محدب و مجموعه اصلی را نقصان محدب S می نامند که برای توصیف اشیاء مهم بوده و در فصل Segmentation مورد بررسی قرار می گیرد.

15. Convex Hull

16. Table of Contents 9.5 Some Basic Morphological Algorithm
9.5.1 Boundary Extraction
9.5.2 Hole Filling
9.5.3 Extraction of Connected Components
9.5.4 Convex Hull
9.5.5 Thinning
9.5.6 Thickening
9.5.7 Skeletons
9.5.8 Pruning
9.5.9 Morphological Reconstruction
Summary of Morphological Operations on Binary Images

17. Thinning

18. Thinning

19. Table of Contents 9.5 Some Basic Morphological Algorithm
9.5.1 Boundary Extraction
9.5.2 Hole Filling
9.5.3 Extraction of Connected Components
9.5.4 Convex Hull
9.5.5 Thinning
9.5.6 Thickening
9.5.7 Skeletons
9.5.8 Pruning
9.5.9 Morphological Reconstruction
Summary of Morphological Operations on Binary Images

20. Thickening
where B is a structuring element suitable for thickening. As in thinning. thickening can be defined as a sequential operation:
The structuring elements used for thickening have the same form as those shown in Fig. 9.2l(a). but with all 1s and 0s interchanged. However, a separate algorithm for thickening is seldom used in practice. Instead, the usual procedure is to thin the background of the set in question and then complement the result. In other words. to thicken a set A. we form C = AC, thin C, and then form C C. Figure 9.22 illustrates this procedure.
Depending on the nature of A. this procedure can result in disconnected points, as Fig. 9.22(d) shows. Hence thickening by this method usually is followed by postprocessing to remove disconnected points Note from Fig. 9.22(c) that the thinned background forms a boundary for the thickening process
This useful feature is not present in the direct implementation of thickening using Eq. (9.5-I0). and it is one of the principal reasons for using background thinning to accomplish thickening.

21. Thickening

22. Table of Contents 9.5 Some Basic Morphological Algorithm
9.5.1 Boundary Extraction
9.5.2 Hole Filling
9.5.3 Extraction of Connected Components
9.5.4 Convex Hull
9.5.5 Thinning
9.5.6 Thickening
9.5.7 Skeletons
9.5.8 Pruning
9.5.9 Morphological Reconstruction
Summary of Morphological Operations on Binary Images

23. Skeleton
Skeleton

24. Applications
Simplify a shape by pruning its skeleton:

25. Skeletons
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.
How this works:
imagine that the foreground regions in the input binary image are made of some uniform slow-burning material. Light fires simultaneously at all points along the boundary of this region and watch the fire move into the interior. At points where the fire traveling from two different boundaries meets itself, the fire will extinguish itself and the points at which this happens form the so called `quench line'. This line is the skeleton.

26. Skeleton of a rectangle defined in terms of bi-tangent circles.
Skeletons
Skeleton of a rectangle defined in terms of bi-tangent circles.

27. Skeletons The skeleton/MAT can be produced in two main ways.
1. to use some kind of morphological thinning that successively erodes away pixels from the boundary (while preserving the end points of line segments) until no more thinning is possible, at which point what is left approximates the skeleton.
2. to calculate the distance transform of the image. The skeleton then lies along the singularities (i.e. creases or curvature discontinuities) in the distance transform.

28. The Hit-or-Miss Transformation
Dilation
Erosion
Closing
Opening
The Hit-or-Miss Transformation

29. Skeletons Opening Erosion Fig. 9.23 shows a skeleton S(A) of a set A.
(a) lf z is a point of S(A) and (D)z is the largest disk cantered at z and contained in A. one cannot find a larger disk (not necessarily centered at z) containing (D)z and included in A. The disk (D)z is called a maximum disk.
(b) The disk (D)Z touches the boundary of A at two or more different places.
Erosion
Opening

30. Skeletons

31. Skeletons

32. Distance Transform
The distance transform of a simple shape. Note that we are using the `chessboard' distance metric.
The distance transform is an operator normally only applied to binary images. The result of the transform is a graylevel image that looks similar to the input image, except that the graylevel intensities of points inside foreground regions are changed to show the distance to the closest boundary from each point.

33. Distance Transform

34. Table of Contents 9.5 Some Basic Morphological Algorithm
9.5.1 Boundary Extraction
9.5.2 Hole Filling
9.5.3 Extraction of Connected Components
9.5.4 Convex Hull
9.5.5 Thinning
9.5.6 Thickening
9.5.7 Skeletons
9.5.8 Pruning
9.5.9 Morphological Reconstruction
Summary of Morphological Operations on Binary Images

35. Pruning

36. Pruning Iteratively prune an image:

37. Applictions Count the legs of a centipede Find the loops of a graph:

38. Applictions
Solve a maze puzzle by thinning all paths and pruning dead ends:

39. The Hit-or-Miss Transformation
Pruning
Thinning
The Hit-or-Miss Transformation

40. The Hit-or-Miss Transformation
Pruning
Thinning
The Hit-or-Miss Transformation
Dilation
H = 3x3 structuring element of 1’s

41. Pruning

42. Table of Contents 9.5 Some Basic Morphological Algorithm
9.5.1 Boundary Extraction
9.5.2 Hole Filling
9.5.3 Extraction of Connected Components
9.5.4 Convex Hull
9.5.5 Thinning
9.5.6 Thickening
9.5.7 Skeletons
9.5.8 Pruning
9.5.9 Morphological Reconstruction
Summary of Morphological Operations on Binary Images

43. Table of Contents 9.5 Some Basic Morphological Algorithm
9.5.1 Boundary Extraction
9.5.2 Hole Filling
9.5.3 Extraction of Connected Components
9.5.4 Convex Hull
9.5.5 Thinning
9.5.6 Thickening
9.5.7 Skeletons
9.5.8 Pruning
9.5.9 Morphological Reconstruction
Summary of Morphological Operations on Binary Images

44. Morphological Operations on Binary Images

45. Morphological Operations on Binary Images

46. Morphological Operations on Binary Images