ch9 - morphological image processing
TRANSCRIPT
Morphological Image Processing
Chapter 9
Introduction
The word morphology commonly denotes a branch of biology that deals with the form and structure of animals and plants.
Therefore, morphological operations are intended to affect the shape of the object
We use mathematical morphology as a tool for extracting image components that are useful in the representation and description of region shape, such as boundaries, skeletons.
intended to affect the shape of the object
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 imagewe will consider:
– What is morphology?– Simple morphological operations– Compound operations– Morphological algorithms
1, 0, Black, White?
Throughout all of the following slides whether 0 and 1 refer to white or black is a little interchangeableAll 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 pixelsAfter this it doesn’t matter if 0 is black, white, yellow, green…….
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 imageMorphological operations are typically applied to remove imperfections introduced during segmentation, and so typically operate on bi-level images
Quick Example
Image after segmentation Image after segmentation and morphological processing
Mathematic Morphology• used to extract image components that are useful in
the representation and description of region shape, such as – boundaries extraction– boundaries extraction– skeletons– convex hull– morphological filtering– thinning– pruning
Mathematic Morphology
mathematical framework used for:• pre-processing
– noise filtering, shape simplification, ...• enhancing object structure• enhancing object structure
– skeletonization, convex hull...• Segmentation
– watershed,…• quantitative description
– area, perimeter, ...
Basic Set Theory
Z2 and Z3
• set in mathematic morphology represent objects in an image– binary image (0 = white, 1 = black) : the element – 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
Structuring element (SE)
§ small set to probe the image under study§ for each SE, define origo§ shape and size must be adapted to geometric§ shape and size must be adapted to geometricproperties for the objects
the origo is the reference point on which deictic relationships are based.
Structuring Elements, Hits & Fits
B Structuring Element
Fit: All on pixels in the structuring element cover on
AC
structuring element cover on pixels in the imageHit: Any on pixel in the structuring element covers an on pixel in the image
All morphological processing operations are based on these simple ideas
Reflection and Translation
} ,|{ˆ Bfor bbwwB ∈−∈=
} ,|{)( Afor azaccA z ∈+∈= } ,|{)( Afor azaccA z ∈+∈=
Logic Operations
Example:
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
operationoperation
Fundamental Operations
Fundamentally morphological image processing is very like spatial filteringThe structuring element is moved across every pixel in the original image to give a pixel in a new processed imageThe value of this new pixel depends on the operation performedThere are two basic morphological operations: erosion and dilation
Basic morphological operations
• Erosion
• Dilation• Dilation
• combine to– Opening object
– Closening background
keep general shape but smooth with respect to
Erosion• Erosion is one of the two basic operators in the area of mathematical morphology , the other being dilation.
• It is typically applied to binary image, but there are versions that work on gray scale image. are versions that work on gray scale image.
• The basic effect of the operator on a binary image is to erode away the boundaries of regions of foreground pixels (i.e. white pixels, typically).
• Thus areas of foreground pixels shrink in size, and holes within those areas become larger.
Erosion
Erosion of image f by structuring element s is given by f � sThe 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),(
fsyxg
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=zin A such that B is in A when origin of B=z
shrink the object
}{ Az|(B)BA z ⊆=−
Erosion
Erosion
}Az|(B){BA z ⊆=−
Effect of erosion using a 3×3 square structuring element
Applications of erosion : Eliminating unwanted detail in an image
Erosion : eliminating irrelevant detail
structuring element B = 13x13 pixels of gray level 1
Erosion Application 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
Erosion Application 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
What Is Erosion For?
Erosion can split apart joined objects
Erosion can split apart
Erosion can strip away extrusions
Watch out: Erosion shrinks objects
Erosion can split apart
Erosion, matlab exampleoriginalBW = imread('circles.png'); se = strel('disk',11); erodedBW = imerode(originalBW,se);imshow(originalBW), figure, imshow(erodedBW)
Dilation• Dilation is one of the two basic operators in the area of mathematical morphology , the other being erosion.
• It is typically applied to binary image, but there are versions that work on gray scale image.
• The basic effect of the operator on a binary image is to gradually enlarge the boundaries of regions of foreground pixels (i.e. white pixels, typically).
• Thus areas of foreground pixels grow in sizewhile holes within those regions become smaller.
DilationDilation of image f by structuring element s is given by f sThe 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),(
fsyxg
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=zin A such that B hits A when origin of B=z
• grow the object
}ˆ{ ΦA)Bz|(BA z ≠∩=⊕ ∩
Dilation
Dilation
Dilation
}ˆ{ ΦA)Bz|(BA z ≠∩=⊕
B = structuring element
∩
Effect of dilation using a 3×3 square structuring element
Applications of dilaion : For bridging gaps in an image
An example:
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!
Dilation : Bridging gaps
Dilation, matlab examplebw = imread('text.png');se = strel('line',11,90);bw2 = imdilate(bw,se);imshow(bw), title('Original')figure, imshow(bw2), title('Dilated'
useful
• Erosion– removal of structures of certain shape and size,
given by SEgiven by SE• Dilation
– filling of holes of certain shape and size, given by SE
Combining erosion and dilation
• WANTED:– remove structures / fill holes– without affecting remaining parts– without affecting remaining parts
• SOLUTION:• combine erosion and dilation• (using same SE)
Duality
∧
−=⊕ BA)BA( cc
∧
⊕=− BABA c
−=⊕ BA)BA( cc
Compound Operations
More interesting morphological operations can be performed by performing combinations of erosions and dilationsThe most widely used of these compound operations are:
– Opening
– Closing B)BA(BA
B)BA(BA
−⊕=•
⊕−=o
Opening and Closing
· Opening and Closing are morphological operations which are based on dilation and erosion.
· Opening smoothes the contours of objects, breaks narrow isthmuses and eliminates thin breaks narrow isthmuses and eliminates thin protrusions.
· Closing also produces the smoothing of sections of contours but fuses narrow breaks, fills gaps in the contour and eliminates small holes.
· Opening is basically erosion followed by dilationwhile closing is dilation followed by erosion.
How OpeningWorks ?· An opening is defined as an erosion followed by a dilationusing the same structuring element for both operations.
Effect of opening using a 3×3 square structuring element
Opening Operation Opening Operation
BBABA ⊕= ) (o
( ) ( ){ }ABBBA zz ⊆∪=o
or
= Combination of all parts of A that can completely contain B
∩
(Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.
Opening eliminates narrow and small details and corners.
Opening effectOriginal Image Processed Image
Structuring Element
Opening example 2
Opening Operation: application
Structuring element is an 11pixel array with a circle shape
Input Image Resultant Image
Opening Operation, matlab example• I = imread('snowflakes.png');figure; imshow(I)
• BW = IM2BW(I); figure; imshow(BW)
• se = strel('disk',3);• I_opened = imopen(BW ,se);• figure, imshow(I_opened)
How Closing Works?
· Grey level closing consists straightforwardly of a grey level dilation followed by a grey level erosion.
· The closing operator therefore requires two inputs: an image to be closed, and a structuring element.
Effect of closing using a 3×3 square structuring element
Closing effectOriginal Image Processed Image
Structuring Element
Closing Operation Closing Operation
BBBA )A( ⊕=•
(Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.
Closing fills narrow gaps and notches
Closing example 2:
Application
Structure element is in between the smaller circle and large circle
Closing Operation, matlab example
• originalBW = imread('circles.png');• imshow(originalBW);
• se = strel('disk',10);• closeBW = imclose(originalBW,se);• figure, imshow(closeBW)
Example: Application of Morphological OperationsExample: Application of Morphological Operations
Finger print enhancement
(Images from Rafael C.Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.
Duality Between Opening and ClosingDuality Between Opening and Closing
( ) ( )BABA cc ˆo=•
Properties OpeningProperties Opening
( )BDBCDC
ABA
oo
o
=
⊂⊂
⊆
then If 2.
.1
( ) BABBA ooo = .3
( ) BABBA
BDBCDC
BAA
•=••
•⊂•⊂
•⊆
.3
then If 2.
.1Properties ClosingProperties Closing
Idem potent property: can’t change any more
Hit or Miss Transform
Basic operation where all other operatorscan be derived
It Hit or miss transform of A by B is denoted by
A B
The difference is in structure element from others
*
HitHit--oror--Miss Transformation Miss Transformation
where X = shape to be detectedW = window that can contain X
( ) [ ])XW( AX AB A c −∩=* ∩
HitHit--oror--Miss Transformation (cont.) Miss Transformation (cont.)
( ) [ ])XW( AX AB A c −∩=* ∩
• How It Works ?
• The structural elements used for Hit-or-miss transforms are an extension to the ones used with dilation, erosion etc.
• The structural elements can contain both foreground and background pixels, rather than just foreground pixels, i.e. both ones and zeros.
• The structuring element is superimposed over each pixel in the • The structuring element is superimposed over each pixel in the input image, and if an exact match is found between the foreground and background pixels in the structuring element and the image, the input pixel lying below the origin of the structuring element is set to the foreground pixel value. If it does not match, the input pixel is replaced by the background pixel value.
Structuring elements
Example
Boundary Extraction Boundary Extraction
( )BAAβ(A) −=
Original image
Boundary
Region (Hole) Filling Region (Hole) Filling
( ) ckk ABXX ∩⊕= −1
where X0 = seed pixel p
∩ k = 1, 2, 3, …..
The iteration stops at step k if Xk = Xk-1
Original image
Results of region filling
p
Extraction of Connected Components Extraction of Connected Components
( ) ABXX kk ∩⊕= −1
The iteration stops at step k if Xk = Xk-1
∩ k = 1, 2, 3, …..
where X0 = seed pixel p
Example: Extraction of Connected Components Example: Extraction of Connected Components
X-ray imageof bones
ThresholdedThresholdedimage
Connectedcomponents
Convex Hull Convex Hull
Convex hull has no concave part.
Convex hull
i
i
DAC ∪=
=4
1
)(
( ) 4,3,2,1 , 1 =∪= − iABXX ik
ik *
iconv
i XD =Algorithm: where
Example: Convex Hull Example: Convex Hull
Thinning Thinning
cBAA
BAABA
) (
) (
∩=
−=⊗ *
*
{ } ))...))((...(( 21 nBBBABA ⊗⊗⊗=⊗
Example: Thinning Example: Thinning
Make an object thinner.
Thickening Thickening
* ) ( BAABA ∪=.
{ } ) )...) ) ((...(( 21 nBBBABA =. . . .*
Make an object thicker
Skeletons Skeletons
Dot lines are skeletons of thisstructurestructure
Skeletons (cont.) Skeletons (cont.)
)()(0
ASAS k
K
k∪=
=
with BkB)AkB)AASk o ( ()( −=
where ...) ) ) (...( ( BBBAkB)A =
k times
and ( ){ }φ≠= kBAkK max
Skeletons Skeletons
Pruning Pruning
{ }BAX ⊗=1
AHXX ∩⊕= )( 23
) ( 1
8
12
k
k
BXX ∪=
= *
= thinning
= finding end points
= dilation at end points
314 XXX ∪= = Pruned result
Example: Pruning Example: Pruning
Originalimage
After Thinning End
Prunedresult
Thinning3 times
End points
Dilationof end points
Summary of Binary Morphological Operations Summary of Binary Morphological Operations
Summary of Binary Morphological Operations (cont.) Summary of Binary Morphological Operations (cont.)
Summary of Binary Morphological Operations (cont.) Summary of Binary Morphological Operations (cont.)
Summary of Binary Morphological Operations (cont.) Summary of Binary Morphological Operations (cont.)
Basic Types of Structuring ElementsBasic Types of Structuring Elements
x = don’t care
GrayGray--Scale Dilation Scale Dilation
{ }bf DyxDytxsyxbytxsfbf ∈∈−−+−−=⊕ ),(;)(),(|),(),(max
{ }bf DxDxsxbxsfbf ∈∈−+−=⊕ and )(|)()(max2-D Case
1-D Case
SubimageOriginal image
Moving
Max
GrayGray--Scale Dilation (cont.) Scale Dilation (cont.)
+
Reflectionof B
window
Output image
Structuring element B
Note: B can be any shape and subimage must have the same shape asreflection of B.
GrayGray--Scale ErosionScale Erosion
{ }bf DyxDytxsyxbytxsfbf ∈∈++−++=− ),(;)(),(|),(),(min2-D Case
1-D Case { }bf DxDxsxbxsfbf ∈∈+−+=− and )(|)()(min
SubimageOriginal image
Moving
Min
GrayGray--Scale Erosion (cont.) Scale Erosion (cont.)
-B
Moving window
Output image
Structuring element B
Note: B can be any shape and subimage must have the same shape as B.
Example: GrayExample: Gray--Scale Dilation and ErosionScale Dilation and ErosionOriginal image After dilation
After erosion
Darker Brighter
GrayGray--Scale OpeningScale Opening
bbfbf ⊕−= )(o
Opening cuts peaks
GrayGray--Scale ClosingScale Closing
bbfbf −⊕=• )(
Closing fills valleys
Example: GrayExample: Gray--Scale Opening and ClosingScale Opening and Closing
Original image After closingAfter opening
Reduce whiteobjects
Reduce darkobjects
GrayGray--scale Morphological Smoothing scale Morphological Smoothing
Smoothing: Perform opening followed by closing
Original image After smoothing
Morphological Gradient Morphological Gradient
)()( bfbfg −−⊕=
Original image Morphological Gradient
TopTop--Hat Transformation Hat Transformation
)( bffh o−=
Original image Results of top-hat transform
Example: Texture Segmentation Application Example: Texture Segmentation Application
Small blob
Algorithm:1. Perform closing on the image by using successively larger structuring elements until small blobs are vanished.2. Perform opening to join large blobs together3. Perform intensity thresholding
Original image Segmented result
Large blob
Example: Granulometry Example: Granulometry Objective: to count the number of particles of each sizeMethod:1. Perform opening using structuring elements of increasing size2. Compute the difference between the original image and the result
after each opening operation3. The differenced image obtained in Step 2 are normalized and used
to construct the size-distribution graph.
Original imageSize distribution
graph