DigitalImageProcessing 9-Morphology.ppt

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Morphological Image Processing
Digital Image Processing

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Contents
Once segmentation is complete,
morphological operations can be used to
remove imperfections in the segmented
image and provide information on the form
and structure of the image
In this lecture we will consider
– What is morphology?
– Simple morphological operations
– Compound operations
– Morphological algorithms

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1, 0, Black, White?
Throughout all of the following slides
whether 0 and 1 refer to white or black is a
little interchangeable
All of the discussion that follows assumes
segmentation has already taken place and
that images are made up of 0s for
background pixels and 1s for object pixels
After this it doesn’t matter if 0 is black, white,
yellow, green…….

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What Is Morphology?
Morphological image processing (or
morphology) describes a range of image
processing techniques that deal with the
shape (or morphology) of features in an
image
Morphological operations are typically
applied to remove imperfections introduced
during segmentation, and so typically
operate on bi-level images

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Quick Example
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)
Image after segmentation Image after segmentation and
morphological processing

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Structuring Elements, Hits & Fits
B
A
C
Structuring Element
Fit: All on pixels in the
structuring element cover
on pixels in the image
Hit: Any on pixel in the
structuring element covers
an on pixel in the image
All morphological processing operations are based
on these simple ideas

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Structuring Elements
Structuring elements can be any size and
make any shape
However, for simplicity we will use
rectangular structuring elements with their
origin at the middle pixel
1 1 1
1 1 1
1 1 1
0 0 1 0 0
0 1 1 1 0
1 1 1 1 1
0 1 1 1 0
0 0 1 0 0
0 1 0
1 1 1
0 1 0

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Fitting & Hitting
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 1 1 0 0 0 0 0 0 0
0 0 1 1 1 1 1 0 0 0 0 0
0 1 1 1 1 1 1 1 0 0 0 0
0 1 1 1 1 1 1 1 0 0 0 0
0 0 1 1 1 1 1 1 0 0 0 0
0 0 1 1 1 1 1 1 1 0 0 0
0 0 1 1 1 1 1 1 1 1 1 0
0 0 0 0 0 1 1 1 1 1 1 0
0 0 0 0 0 0 0 0 0 0 0 0
B C
A
1 1 1
1 1 1
1 1 1
Structuring
Element 1
0 1 0
1 1 1
0 1 0
Structuring
Element 2

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Fundamental Operations
Fundamentally morphological image
processing is very like spatial filtering
The structuring element is moved across
every pixel in the original image to give a
pixel in a new processed image
The value of this new pixel depends on the
operation performed
There are two basic morphological
operations: erosion and dilation

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Erosion
Erosion of image f by structuring element s
is given by f  s
The structuring element s is positioned with
its origin at (x, y) and the new pixel value is
determined using the rule:




otherwise
0
fits
if
1
)
,
(
f
s
y
x
g

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Erosion Example
Structuring Element
Original Image Processed Image With Eroded Pixels

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Erosion Example
Structuring Element
Original Image Processed Image

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Erosion Example 1
Watch out: In these examples a 1 refers to a black pixel!
Original image Erosion by 3*3
square structuring
element
Erosion by 5*5
square structuring
element

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Erosion Example 2
Original
image
After erosion
with a disc of
radius 10
After erosion
with a disc of
radius 20
After erosion
with a disc of
radius 5
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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What Is Erosion For?
Erosion can split apart joined objects
Erosion can strip away extrusions
Watch out: Erosion shrinks objects
Erosion can split apart

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Dilation
Dilation of image f by structuring element s is
given by f s
The structuring element s is positioned with
its origin at (x, y) and the new pixel value is
determined using the rule:





otherwise
0
hits
if
1
)
,
(
f
s
y
x
g

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Dilation Example
Structuring Element

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Dilation Example
Structuring Element
Original Image Processed Image With Dilated Pixels

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Dilation Example 1
Original image Dilation by 3*3
square structuring
element
Dilation by 5*5
square structuring
element
Watch out: In these examples a 1 refers to a black pixel!

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Dilation Example 2
Structuring element
Original image After dilation
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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What Is Dilation For?
Dilation can repair breaks
Dilation can repair intrusions
Watch out: Dilation enlarges objects

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Compound Operations
More interesting morphological operations
can be performed by performing
combinations of erosions and dilations
The most widely used of these compound
operations are:
– Opening
– Closing

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Opening
The opening of image f by structuring
element s, denoted f ○ s is simply an erosion
followed by a dilation
f ○ s = (f s) s

Original shape After erosion After dilation
(opening)
Note a disc shaped structuring element is used
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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Opening Example
Original
Image
Image
After
Opening
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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Opening Example
Structuring Element

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Opening Example
Structuring Element

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Closing
The closing of image f by structuring
element s, denoted f • s is simply a dilation
followed by an erosion
f • s = (f s)s

Original shape After dilation After erosion
(closing)
Note a disc shaped structuring element is used
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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Closing Example
Original
Image
Image
After
Closing
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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Closing Example
Structuring Element

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Closing Example
Structuring Element

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Morphological Processing Example
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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Morphological Algorithms
Using the simple technique we have looked
at so far we can begin to consider some
more interesting morphological algorithms
We will look at:
– Boundary extraction
– Region filling
There are lots of others as well though:
– Extraction of connected components
– Thinning/thickening
– Skeletonisation

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Boundary Extraction
Extracting the boundary (or outline) of an
object is often extremely useful
The boundary can be given simply as
β(A) = A – (AB)
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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Boundary Extraction Example
A simple image and the result of performing
boundary extraction using a square 3*3
structuring element
Original Image Extracted Boundary
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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Region Filling
Given a pixel inside a boundary, region filling
attempts to fill that boundary with object
pixels (1s)
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)
Given a point inside
here, can we fill the
whole circle?

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Region Filling (cont…)
The key equation for region filling is
Where X0 is simply the starting point inside
the boundary, B is a simple structuring
element and Ac is the complement of A
This equation is applied repeatedly until Xk
is equal to Xk-1
Finally the result is unioned with the original
boundary
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)
.....
3
,
2
,
1
)
( 1 


  k
A
B
X
X c
k
k

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Region Filling Step By Step
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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Region Filling Example
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)
Original Image One Region
Filled
All Regions
Filled

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Summary
The purpose of morphological processing is
primarily to remove imperfections added during
segmentation
The basic operations are erosion and dilation
Using the basic operations we can perform
opening and closing
More advanced morphological operation can
then be implemented using combinations of all
of these

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Structuring Elements, Hits & Fits

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Erosion Example
Structuring Element

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Dilation Example
Structuring Element

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Opening Example
Structuring Element

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Closing Example
Structuring Element

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Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

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of
39
Images
taken
from
Gonzalez
&
Woods,
Digital
Image
Processing
(2002)

DigitalImageProcessing 9-Morphology.ppt

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