Chapter_9_Morphological_Image_Processing.ppt

Chapter 9: Morphological
Image Processing
Digital Image Processing

2
Mathematic Morphology
 used to extract image components that are
useful in the representation and description of
region shape, such as
 boundaries extraction
 skeletons
 convex hull
 morphological filtering
 thinning
 pruning

3
Mathematic Morphology
mathematical framework used for:
 pre-processing
 noise filtering, shape simplification, ...
 enhancing object structure
 skeletonization, convex hull...
 Segmentation
 watershed,…
 quantitative description
 area, perimeter, ...

4
Z2
and Z3
 set in mathematic morphology represent
objects in an image
 binary image (0 = white, 1 = black) : the
element of the set is the coordinates (x,y)
of pixel belong to the object  Z2
 gray-scaled image : the element of the set
is the coordinates (x,y) of pixel belong to the
object and the gray levels  Z3

6
Reflection and Translation
}
,
|
{
ˆ B
for b
b
w
w
B 



}
,
|
{
)
( A
for a
z
a
c
c
A z 




Structuring element (SE)
9
 small set to probe the image under study
 for each SE, define origo
 shape and size must be adapted to geometric
properties for the objects

Basic idea
 in parallel for each pixel in binary image:
 check if SE is ”satisfied”
 output pixel is set to 0 or 1 depending on
used operation
10

How to describe SE
 many different ways!
 information needed:
 position of origo for SE
 positions of elements belonging to SE
11

Basic morphological operations
 Erosion
 Dilation
 combine to
 Opening object
 Closening background
12
keep general shape but
smooth with respect to

Erosion
 Does the structuring element fit the
set?
erosion of a set A by structuring element
B: all z in A such that B is in A when
origin of B=z
shrink the object
13
}
{ A
z|(B)
B
A z 



17
Erosion
}
{ A
z|(B)
B
A z 



Dilation
 Does the structuring element hit the set?
 dilation of a set A by structuring element
B: all z in A such that B hits A when origin
of B=z
 grow the object
18
}
ˆ
{ Φ
A
)
B
z|(
B
A z 




22
Dilation
}
ˆ
{ Φ
A
)
B
z|(
B
A z 



B = structuring element

useful
 erosion
 removal of structures of certain shape and
size, given by SE
 Dilation
 filling of holes of certain shape and size,
given by SE
24

Combining erosion and
dilation
 WANTED:
 remove structures / fill holes
 without affecting remaining parts
 SOLUTION:
 combine erosion and dilation
 (using same SE)
25

26
Erosion : eliminating irrelevant
detail
structuring element B = 13x13 pixels of gray level 1

Opening
erosion followed by dilation, denoted ∘
 eliminates protrusions
 breaks necks
 smoothes contour
27
B
B
A
B
A 

 )
(


30
Opening
B
B
A
B
A 

 )
(

}
)
(
|
)
{( A
B
B
B
A z
z 




Closing
dilation followed by erosion, denoted •
 smooth contour
 fuse narrow breaks and long thin gulfs
 eliminate small holes
 fill gaps in the contour
31
B
B
A
B
A 


 )
(

34
Closing
B
B
A
B
A 


 )
(

35
Properties
Opening
(i) AB is a subset (subimage) of A
(ii) If C is a subset of D, then C B is a subset of D B
(iii) (A B) B = A B
Closing
(i) A is a subset (subimage) of AB
(ii) If C is a subset of D, then C B is a subset of D B
(iii) (A B) B = A B
Note: repeated openings/closings has no effect!

Duality
 Opening and closing are dual with respect
to complementation and reflection
36
)
ˆ
(
)
( B
A
B
A c
c




Hit-or-Miss Transformation
(HMT)
⊛
 find location of one shape among a set of shapes
”template matching
 composite SE: object part (B1) and background
part (B2)
 does B1 fits the object while, simultaneously,
B2 misses the object, i.e., fits the background?
41

42
Hit-or-Miss Transformation
)]
(
[
)
( X
W
A
X
A
B
A c







43
Boundary Extraction
)
(
)
( B
A
A
A 




45
Region Filling
,...
3
,
2
,
1
)
( 1 


  k
A
B
X
X c
k
k

47
Extraction of connected
components

Convex hull
 A set A is is
said to be
convex if
the straight
line segment
joining any
two points
in A lies
entirely
within A.
i
i
D
A
C
4
1
)
(



,...
3
,
2
,
1
and
4
,
3
,
2
,
1
)
( 



 k
i
A
B
X
X i
i
k
i
k
49

51
Thinning
c
B
A
A
B
A
A
B
A
)
(
)
(








52
Thickening
)
( B
A
A
B
A 




53
Skeletons
K
k
k A
S
A
S
0
)
(
)
(



B
kB
A
kB
A
A
Sk 
)
(
)
(
)
( 



}
)
(
|
max{ 


 kB
A
k
K
)
)
(
(
0
kB
A
S
A k
K
k





55
Pruning
}
{
1 B
A
X 

A
H
X
X 

 )
( 2
3
3
1
4 X
X
X 

H = 3x3 structuring element of 1’s
)
( 1
8
1
2
k
k
B
X
X 




60
5 basic structuring elements

Chapter_9_Morphological_Image_Processing.ppt

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