IRJET- Image Restoration using Adaptive Median Filtering
final presentation
1. Supervisors
Assis. Prof. Hossam El-Din Moustafa
Eng. Khaled M. Abd El-Fattah
Mansoura University
Faculty of Engineering
Dept. of Electronics and
Communications Engineering
2. Aims of The Present Work
1. To evaluate the performance of different
image de-noising techniques using
simulated data set.
2. To design a digital system to process and
register digital Computed Tomography
(CT) and Magnetic Resonance (MR)
images for the human's brain.
3. To compare the performance of medical
image registration techniques.
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3. Aims of The Present Work
4. To implement two image fusion
techniques and compare them according
to two measures of performance.
5. To apply image fusion techniques to the
following medical applications:
Detection of acute intra-cerebral
hemorrhage.
Detection of hepatic lesions.
Detection of head & neck cancer.
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4. Project Outline
Chapter 1 gives an introduction to the
project.
Chapter 2 describes types of noise and
image de-noising techniques.
Chapter 3 introduces the theoretical basis
of image registration techniques.
Chapter 4 presents biomedical applications
of image registration.
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5. Project Outline
Chapter 5 gives a review to the main types of
image fusion techniques.
Chapter 6 presents the different applications
of image fusion techniques.
Chapter 7 summarizes the concluded remarks
and the future work.
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6. The Need For Image Registration
To solve the problem of misalignment in
acquired images due to (camera movement –
object movement – different resolutions - …).
Clinicians seek to integrate the
complementary information provided by
different modalities.
This leads to improved diagnostic accuracy
and treatment effectiveness.
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8. The Need For Image Fusion
Image fusion is the process by which two
or more images are combined into a
single image retaining the important
features from each of the source images.
Image fusion aims at the integration of
complementary data to enhance the
information apparent in the images.
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11. Digital Image Simulation
The original image is cameraman image of size
(256×256) .
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12. Noise Simulation
Real images usually contain departures
from ideality which are referred to as
noise or artifacts.
Four different artifacts will be simulated;
Gaussian noise, salt & pepper noise,
speckle noise, and composite noise,
which is a scaled version of the first three
types.
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13. Gaussian Noise
Gaussian noise is an idealized form of
white noise, which is caused by random
fluctuations in the signal.
White noise is a random signal with flat
power spectral density which is normally
distributed.
Gaussian noise with zero mean and 0.01
variance was added to the original image.
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15. Salt and Pepper Noise
Salt & pepper noise can be represented as
on/off pixels.
This degradation can be caused by sharp,
sudden disturbances in the image signal.
The noise is usually quantified by the
percentage of pixels which are corrupted.
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16. The Original Image and Salt & Pepper
Noise
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17. Speckle Noise
It is modeled with a multiplicative or nonlinear
model using the following equation:
Where I is the pure image, J is the output
image, and n is a uniformly distributed random
noise with zero-mean.
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InIJ
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19. Composite Noise
During the imaging process all types of
artifacts are present and affect the image
quality.
Composite noise is a scaled version of
the first three types of noise.
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21. Image De-noising
Two main image de-noising techniques
will be discussed:
Spatial domain filters.
Transformed domain filters.
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23. I. Mean Filter
Replace each pixel value in an image
with the mean value of its neighbors,
including itself .
It is based on a Kernel (mask), which
represents the shape and size of the
neighborhood to be sampled when
calculating the mean; for example:
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111
111
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25. Project Group 2010 25
II. Gaussian Filter
Gaussian smoothing is a
2D convolution operator
that is used to remove
noise.
It is similar to the mean
filter, but it uses a
different Kernel that
represents the shape of a
Gaussian hump.
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27. Non-Linear Filters
A nonlinear filter is an image processing
device whose output is not a linear function
of its input.
Nonlinear filter locates and removes data
that is recognized as noise.
The algorithm is nonlinear because it looks
at each data point and decides if that data is
noise or valid information.
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28. I. Wiener Filters
The Wiener filter is: non Linear, adaptive,
and time-invariant.
It minimizes the mean-squared value of the
error (MSE); that is defined as the
difference between desired response and the
actual filter output.
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30. II. Median Filter
The median is calculated by first sorting
all the pixel values from the surrounding
neighborhood into numerical order and
then replacing the pixel being considered
with the middle pixel value.
If the neighborhood under consideration
contains an even number of pixels, the
average of the two middle pixel values is
used .
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32. Results of Spatial Domain Techniques
32Project Group 2010
Removing Gaussian noise using spatial domain filters
Output SNR
Filter Type
SNRi= 5SNRi= 2
28.901314.3603Standard Mean
11.77764.7651Gaussian
21.89109.7658Median
36.922920.6862Wiener
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33. Results of Spatial Domain Techniques
33Project Group 2010
Removing salt & pepper noise using spatial domain filters
Output SNR
Filter Type
SNRi=5SNRi=2
27.90613.745Standard Mean
11.6784.7269Gaussian
129.28126.631Median
20.388.6991Wiener
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34. Results of Spatial Domain Techniques
34Project Group 2010
Removing speckle noise using spatial domain filters
Output SNR
Filter Type
SNRi=5SNRi=2
30.07814.855Standard Mean
11.8944.8096Gaussian
15.3826.6718Median
28.41112.829Wiener
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35. Results of Spatial Domain Techniques
35Project Group 2010
Removing composite noise using spatial filters
Output SNR
Filter Type
SNRi=5SNRi=2
28.290213.9694Standard Mean
11.74584.7522Gaussian
26.325912.0890Median
31.161116.9072Wiener
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36. Transformed Techniques
Transformed domain filters process the
digital image in any domain other than
spatial domain. The domain may be
frequency, wavelet, or any other .
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Corrupted
Image
De-noised
Image
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37. Wavelet Filters
The Wavelet transform is a signal analysis
tool that provides a multi-resolution
decomposition of an image and results in a
non-redundant image representation.
This basis consists of wavelets, which are
functions generated from one single
function, called mother wavelet, by dilations
and translations.
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38. Advantages of wavelet transform
Windowing technique with variable sized
regions.
Window will be long time for low frequency.
Window will be short time for high frequency.
Multi-scale.
Shifting.
Variety of bases functions, which will be
selected to fit the application.
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42. Results of Wavelet Filter
42Project Group 2010
Results of Gaussian Noise
Output SNR
Basic function
SNRi=5SNRi=2
26.659817.0601Haar
30.939818.6587Db-3
30.088818.4003Sym-2
32.567419.3754Dmey
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43. Results of Wavelet Filter
43Project Group 2010
Results of Salt & Pepper Noise
Output SNR
Basic function
SNRi=5SNRi=2
6.22462.3096Haar
6.65942.4114Db-3
6.45762.3702Sym-2
6.90912.4788Dmey
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44. Results of Wavelet Filter
44Project Group 2010
Results of Speckle Noise
Output SNR
Basic function
SNRi=5SNRi=2
28.349918.1078Haar
32.338619.3332Db-3
31.845119.3576Sym-2
34.676320.6401Dmey
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45. Results of Wavelet Filter
45Project Group 2010
Results of composite Noise
Output SNR
Basic function
SNRi= 5SNRi= 2
26.156316.5908Haar
29.802217.3886Db-3
29.873717.6782Sym-2
31.891218.8953Dmey
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47. What is Image Registration?
Image registration is the process of
geometrically aligning two or more
images corresponding to the same scene
but taken under different imaging
conditions.
In other words, image registration is the
process of superimposing two images to
find the best transform to make them
match.
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48. Importance of Image
Registration
Face detection.
Estimating intensity differences between
images.
Biomedical image registration.
Image fusion.
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51. Another Example
Description
Transformation
Type
Transformation that can include translation, rotation, scaling, and
shearing. Straight lines remain straight, and parallel lines remain
parallel, but rectangles might become parallelograms.'affine'
Transformation in which straight lines remain straight but parallel
lines converge toward vanishing points. (The vanishing points can fall
inside or outside the image -- even at infinity.)'projective'
Special case of an affine transformation where each dimension is
shifted and scaled independently.'box'
User-defined transformation, providing the forward and/or inverse
functions that are called by imtransform.'custom'
Composition of two or more transformations.'composite'
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52. Taxonomy Of Medical Image
Registration Methodology
I. Dimensionality.
II. Nature of registration basis.
III. Nature of transformation.
IV. Domain of transformation.
V. Interaction.
VI. Optimization procedure.
VII.Modalities involved.
VIII.Subject.
IX. Object.
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53. I. Dimensionality
A. Spatial dimensions only:
1. 2D/2D
2. 2D/3D
3. 3D/3D
B. Time series (more than two images):
1. 2D/2D
2. 2D/3D
3. 3D/3D
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54. II. Nature of Registration Basis
A. Extrinsic.
B. Intrinsic.
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55. III. Nature of The Transformation
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A. Rigid.
B. Affine.
C. Projective.
D. Curved.
56. IV. Domain of The Transformation
A. Local
Subsections have their own.
B. Global
Apply to entire image.
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58. V. Interaction
A. Automatic:
The user only supplies the algorithm with
the data and possibly information on the
image acquisition.
B. Semi-automatic:
The interaction required can be of two
different natures: the user needs to
initialize the algorithm, by segmenting the
data.
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59. V. Interaction
C. Interactive:
The user does the registration himself,
assisted by software supplying a visual
or numerical impression of the current
transformation.
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60. VI. Optimization Procedure
The parameters that make up the
registration transformation can be either:
Directly computed.
Searched for.
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61. VII. Modalities involved
Monomodal.
Multimodal.
Modality to model.
Patient to modality.
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62. VIII. Subject
Intra subject.
Inter subject.
Atlas.
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63. IX. Object
The object of the registered image may be
related to the head.
It can be thorax.
It may also be abdominal.
It can be related to limbs.
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64. Image Registration Techniques
Three different techniques were
implemented:
1. Cross correlation registration.
2. Registration using control points.
3. Registration by maximization of mutual
information (MI).
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65. 1. Cross Correlation
Registration
Cross-correlation is the basic statistical
approach to registration.
It gives a measure of the degree of
similarity between an input image and a
reference one.
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66. 1. Cross Correlation
Registration
For a reference image U, and an input
image V, the two-dimensional normalized
cross-correlation function can be
represented as:
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67. 1. Cross Correlation
Registration
Steps of the algorithm:
Read images and Choose sub-regions of
each image.
Do normalized cross correlation and find
coordinates of peak.
Find total offset between the images.
Shift the test image by the offset to get the
registered image.
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68. 2. Control Points Selection
Registration
control point selection based on selecting
control point pairs from two images , input
image ad base image.
The control point pairs can be selected
either automatically or by the user on the
monitor. Visual selection is a general and
safe method.
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69. 2. Control Points Selection
Registration
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70. 2. Control Points Selection
Registration
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71. 3. Maximization of Mutual
Information Registration
MI is a measure of the amount of
information that one random variable
contains about another random variable.
MI will indicate the best match between a
reference image and an input image.
MI can be described as :
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)()(),( ABHBHBAI
72. 3. Maximization of Mutual
Information Registration
Steps of the algorithm:
1. Calculate (MI) of A and B for each
rotation.
2. For each rotation, calculate (MI) for all
possible translations of the input image to
the reference image.
3. Find the overall maximum MI value. The
coordinates of this maximum value refers
to the rotation and translation.
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73. Performance Criteria
Image quality measures are figures of
merit used for the evaluation of imaging
systems or of coding techniques.
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74. Performance Criteria
Quality metrics are categorized into six groups
according to the type of information they use,
These categories are:
1. Pixel difference-based measures as mean square error.
2. Correlation-based measures as (NCCC).
3. Edge-based measures as edge measure.
4. Spectral distance-based measures as spectral phase error.
5. Context-based measures as rate distortion measure.
6. Human Visual System (HVS)-based measures as
absolute norm.
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75. Normalized Cross Correlation
Coefficient (NCCC)
It can be described by the following
equation:
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76. Weighted Peak Signal-to-Noise
Ratio (WPSNR)
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PSNR=24.6, WPSNR=26.4
PSNR=24.6, WPSNR=27.9
PSNR=24.6, WPSNR=29.3
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78. Introduction on Medical Imaging
Medical imaging play an important role in
medicine as it is used as a diagnostic tools.
It contains various methods including:
Computed Tomography (CT).
Magnetic Resonance Imaging (MRI).
Single-Photon-Emission Tomography (SPECT).
Positron-Emission Tomography (PET).
Ultrasound.
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79. Computed Tomography (CT)
known as CT scan (or CAT scan) stands for
Computerized (Axial) Tomography scan.
Definition:
Is a medical imaging method used to
generate a three-dimensional image of the
inside of an object from a large series of
two-dimensional X-ray images taken
around a single axis of rotation.
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80. Advantage of CT to X-ray
CT scan is prefered to X-ray for some reasons as:
1. The X-ray tube and detector can make 360◦
rotation.
2. It provides 2D and 3D image where X-ray can
provide 2D image only.
3. In one 360◦ lap about 1,000 snapshots which
also called (profiles or slices) are sampled.
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81. CT Mechanism
CT scanning operation is based on the
X-ray principal; As X-rays pass through the
body, They are absorbed or attenuated
(weakened) at differing levels receiving
X-ray beams of different strength which
gives number of different slices, This slices
is registered on film, Thus creating an
image.
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82. CT Mechanism
In the case of CT; The film is replaced
with a detector so the X-ray beams are
detected after they have passed through
the body and their intensity (strength) is
measured from different angles as the
X-ray emitter rotate around the patient.
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83. CT Mechanism
A computer is used to work out the
relative density of the tissues examined.
The computer processes the results,
displaying 3D images on
the monitor.
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85. CT Applications
The CT scanner was
originally designed to
take pictures of the brain.
Now it is much more
advanced and is used for
taking internal pictures of
any part of the body,
which became useful for
testing any bleeding in
the brain, brain tumours
and brain damage.
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Liver CAT scan
Abdominal CAT scan
86. Magnetic Resonance Imaging
(MRI)
Is a test that uses a nuclear magnetic field
and pulses of radio wave energy to make
pictures of organs and structures inside the
body.
Patient will usually be alone on room. The
technologist will be able to see, hear and
speak with you at all time using a two-way
intercom.
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88. Magnetic Resonance Imaging
(MRI)
If patient allergic to any medicines, he must
tell doctor before test.
If patient have artificial limb, metal pins,
metal parts in your body, doctor tools, any
medicine patches or accessories must get
rid of them before test.
You need to lie very still inside the MRI
magnet. You may need medicine (sedative)
to help you relax.
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89. Magnetic Resonance Imaging
(MRI)
How the test is performed?
Put the patient on the table in the big
magnetic field.
Transmit radio waves into patient and
receive it.
Store measured radio waves.
Process raw data to computer to
reconstruct image.
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90. Advantages of MRI:
MRI is “noninvasive”, so there is no
exposure to ionizing radiation.
High sensitivity.
Its contrast material does not has any side
effect.
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Magnetic Resonance Imaging
(MRI)
91. Magnetic Resonance Imaging
(MRI)
There are some disadvantages, but they
can overcome them:
Fat patient cannot use it, but they now use
open MRI.
distributed sound caused while device is
working, they can overcome by putting
earplugs on ears of patient.
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92. Simulated Data
Computer simulations are image pairs
generated by computer.
They have a lot of advantages:
Evaluation of registration method is
achieved.
Changing types of distortion is
available.
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93. Simulated Data
The image “cameraman.tif” was selected as
a reference image.
Its size was 256 pixels by 256 pixels.
Five templates were obtained by scaling the
reference image by values between 0.1 and
10.
Five templates were obtained by rotating the
reference image by values between -15 and
15 degrees.
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96. Simulated Data
Three registration techniques were applied
to the simulated data set.
The Normalized Cross Correlation
Coefficient (NCCC) and the Weighted Peak
Signal-to-Noise Ratio (WPSNR) were
calculated for each registered image.
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106. Simulation Results
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Average
NCCC
Average
WPSNR (dB)
Registration
Technique
0.925118.3823
Cross
Correlation
0.952628.001
Control
Points
0.977131.7691
Mutual
Information
107. Application of Registration
Techniques to Medical Images
Real database consists of:
48 CT images with size 804x1005.
128 MR images with size 256x256.
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108. Application of Registration
Techniques to CT Images
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Reference Image Test Image
Difference between registered imagesRegistration using cross correlation
109. Application of Registration
Techniques to CT Images
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Registration using control points Difference between registered images
Reference Image Test Image
110. Application of Registration
Techniques to CT Images
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Registration using MI Difference between registered images
Reference Image Test Image
111. Application of Registration
Techniques to CT Images
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Average
NCCC
Average
WPSNR (dB)
Registration
Technique
0.971126.962
Cross
Correlation
0.951623.647
Control
Points
0.963927.758
Mutual
Information
112. Application of Registration
Techniques to MR Images
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Reference Image Test Image
Difference between registered imagesRegistration using cross correlation
113. Application of Registration
Techniques to MR Images
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Reference Image Test Image
Registration using control points Difference between registered images
114. Application of Registration
Techniques to MR Images
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Reference Image Test Image
Difference between registered imagesRegistration using MI
115. Application of Registration
Techniques to MR Images
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Average
NCCC
Average
WPSNR (dB)
Registration
Technique
0.932126.443
Cross
Correlation
0.865422.454
Control
Points
0.941327.123
Mutual
Information
117. Image fusion is a concept of combining
multiple images into composite products,
through which more information than that of
individual input images can be revealed.
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First Image Second Image Fused Image
What is Image Fusion?
118. Types of Image Fusion System
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Single-sensor image fusion system.
Multi-sensor image fusion system.
119. The sensor shown could be a digital
camera. It captures the real world as a
sequence of images. The sequence is
then fused in one single image.
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Single-Sensor Image Fusion
System
121. Objectives of Image Fusion
Extract all the useful information.
Compact representation of information.
Increased sharpness.
Improve overall colour appearance.
Replace defective data.
Increased reliability.
Enhance certain features that are not
visible in either of the source images.
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122. Applications of Image Fusion
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Remote sensing.
Interpretation and classification of
satellite image.
Military application.
Tracking, night pilot guidance.
Manufacturing application.
Electronic circuit and component inspection.
123. Applications of Image Fusion
Robot vision.
Stereo camera fusion.
Medical imaging .
Fusing CT image and MR image.
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128. Image Fusion Algorithms
Extracts all useful information.
Preserves all relevant information in the
fused image.
Suppresses irrelevant parts of the image
and noise.
Minimizes any artefacts in the fused
image.
132. 2. Using Spatial Frequency
To calculate spatial frequency,
consider an image of size m × n.
m: no. of rows.
n: no. of columns.
The row and column frequencies of the
image are given respectively by:
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133. Spatial Frequency Algorithm
The row frequency (rf) and the column
frequency (cf) of the image are given
respectively by:
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134. Spatial Frequency Algorithm
The higher the spatial frequency, The clearer
the image.
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figure SF
a 28.67
b 17.73
c 12.98
d 10.04
e 7.52
139. Applications of Image Fusion
Fusion of Multi-focus Images.
Fusion of Multi-exposure Images.
Image Fusion for Security Systems.
Image Fusion for Night vision.
Biomedical Application of Image
Fusion.
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140. Application of Fusion Techniques
to Synthetic Data
Image Fusion techniques.
Spatial Frequency technique.
Wavelet transform.
Measures of Performance.
Entropy.
Spatial Frequency.
Test set: 120 images (already registered).
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141. Application of Fusion Techniques
to Synthetic Data
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Image A Image B
Fusion using spatial frequency Fusion using wavelet
142. Application of Fusion Techniques to
Synthetic Data
Algorithm
Entropy S.F
I/P O/P I/P O/P
Wavelet
6.8217 7.2547 31.4926 37.9628
Spatial Frequency 6.8217 6.8393 31.4926 33.4825
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143. Fusion of Multi-exposure Images
Exposure fusion computes the desired
image by keeping only the “best” parts in
the multi-exposure image sequence.
An over- or under-exposed area carries
less information than the same area when
well-exposed.
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144. Fusion of Multi-exposure Images
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Image A
Fusion using spatial frequency
Image B
Fusion using wavelet
145. Fusion of Multi-exposure Images
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Algorithm
Entropy S.F
I/P O/P I/P O/P
Wavelet
7.5509 7.6026 25.6933 27.1031
Spatial
Frequency 7.5509 7.6330 25.6933 28.6547
146. Fusion of Multi-focus Images
Multi-focus image fusion is a process of
combining two or more partially defocused
images.
There are a number of techniques for
multi-focus image fusion.
147. Fusion of Multi-focus Images
Multi focus digital image fusion attempts
to increase the apparent depth of field
through the fusion of object within several
different fields of focus.
150. Image Fusion for Night Vision
Night vision technology has evolved over the past
few decades in two directions:
I. Reflective light sensitive devices image
intensifier tubes, InGaAs, and EMCCD
detectors.
II. Heat sensitive (thermal infrared) devices
Hecate, InSb, and micro bolometer detectors);
both types of devices are essential for night
intelligence, surveillance, and reconnaissance
(ISR) operations.
151. Image Fusion for Night Vision
The images will be collected from different
types of sensors:
visual cameras.
lowlight night vision cameras.
infrared cameras (IR).
millimeter wave (MMW) cameras.
X-ray imagers.
152. Fusion of Night vision images
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154. Image Fusion for Security
Systems
we test the two fusion algorithms on two
images (transmission and backscattering
X-rays data) of a suitcase containing two
guns. Figure shows the original source
images as well as their fused image using
two different image fusion techniques.
157. Biomedical Application of Image
Fusion
It is the fusion of patient images in
different data formats.
These forms can include:
1. Magnetic Resonance Image (MRI).
2. Computed Tomography (CT).
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158. Biomedical Application of Image
Fusion
Fusion of CT and MR Images.
Detection of Intra-Cerebral Hemorrhage.
Detection of Hepatic Lesions.
Image Fusion of Early and Delayed CT
Scans.
159. Fusion of CT and MR Images
CT provides the best
information on denser tissue
with less distortion.
MR provides better information
on soft tissue with more
distortion.
Seven CT and seven MR
images for brain were taken
from the Visible Human
Project.
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160. Fusion of CT and MR Image
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161. Results of CT and MRI
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Spatial
frequency
EntropyImage
10.82485.8344I/P average
12.10726.6148
Fused image
using spatial freq.
12.35347.0345
Fused image
using wavelet
162. Detection of acute Intra-Cerebral
Hemorrhage
CT-MRI image fusion can increase the
accuracy of detection of intra cerebral
hemorrhage. This was achieved by
applying fusion techniques on three cases.
163. Case (A)
In case (A) the hemorrhage was visualized on
MRI, but not on CT.
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165. Case (B)
Case (B) represents regions that were interpreted
as acute hemorrhage on CT but were interpreted as
chronic hemorrhage on MRI.
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167. Case (C)
Case (C) represents regions that were interpreted
as acute hemorrhage on both CT and MRI
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168. Results of (case C)
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Spatial frequencyEntropyImage
17.20455.6180I/P average
17.73186.2828
Fused image
using spatial freq
20.69956.6116
Fused image
using wavelet
169. Detection of Hepatic Lesions
Patients with known hepatic lesions who
were eligible for surgery underwent dual-
phase helical CT and phased array MRI.
MRI imaging found additional lesions not
detected on CT in some patients, while CT
detected additional lesions not seen on
MRI imaging.
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170. Detection of Hepatic Lesions
Fusing MRI and CT images made
complete detection for these lesions.
The two fusion techniques were
applied to six cases. Results were
compared both qualitatively and
quantitatively.
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172. Results of hepatic lesions
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Spatial
frequency
EntropyImage
16.89285.6413I/P average
18.12525.9871
Fused image
using spatial
freq
18.81516.4076
Fused image
using wavelet
173. Image Fusion of Early and
Delayed CT Scans
Image fusion techniques were applied to
facilitate detection of head and neck cancer
using two sets of CT images; early (30 sec
delay), and delayed (180 sec delay) CT images.
The fused CT image depicts combined tumor
enhancement and vessel opacification. This had
led to higher detection accuracy of head and
neck cancer than using conventional CT
images.
174. Image Fusion of Early and
Delayed CT Scans
Recent reports have shown that certain
head and neck tumors.
1. Squamous cell carcinomas.
2. Pleomorphic adenomas.
3. parotid gland and salivary gland tumors.
176. Results of Early and Delay CT
scans
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Spatial frequencyEntropyImage
9.10556.3875I/P average
10.13246.5568
Fused image
using spatial freq
10.33476.8248
Fused image
using wavelet
177. Conclusion
The registration of biomedical images is of high
importance for surgical planning, diagnosis, and
medical research. It is a precursor to image
fusion.
Three different techniques of image registration
were implemented and applied to MR and CT
images.
Maximization of mutual information is an
effective registration tool for applications
involving CT and MR images.
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178. Conclusion
Two image fusion algorithms have been
implemented and compared using two
measures of performance.
Results on a test set of 120 pair of images
have proved that there is no significant
difference between the performance of
the two fusion algorithms.
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179. Conclusion
In biomedical applications, the fused
image has better eye perception than both
source images.
Wavelet technique was the best among
other techniques in applications including
detection of intra-cerebral hemorrhage.
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180. Future Work
The presented techniques can be applied to
more biomedical image sets.
Trying new techniques for image
registration and fusion.
Modifying the presented algorithms to
work properly in 3D applications.
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