time-frequency analysis and wavelet transform oral presentation
DESCRIPTION
Time-Frequency Analysis and Wavelet Transform Oral Presentation. Advisor: 丁建均 and All Class Members Student: 李境嚴 ID: D00945001. What’s Today?. XIII. Applications of Time–Frequency Analysis . (1) Finding Instantaneous Frequency (2) Signal Decomposition (3) Filter Design - PowerPoint PPT PresentationTRANSCRIPT
Time-Frequency Analysis and Wavelet Transform Oral
PresentationAdvisor: 丁建均 and All Class Members
Student: 李境嚴ID: D00945001
What’s Today?
3
(1) Finding Instantaneous Frequency (2) Signal Decomposition(3) Filter Design (4) Sampling Theory (5) Modulation and Multiplexing (6) Electromagnetic Wave Propagation(7) Optics(8) Radar System Analysis (9) Random Process Analysis (10) Music Signal Analysis
(11) Acoustics (12) Biomedical Engineering (13) Spread Spectrum Analysis (14) System Modeling(15) Image Processing(16) Economic Data Analysis (17) Signal Representation (18) Data Compression (19) Seismology(20) Geology
XIII. Applications of Time–Frequency Analysis
Biomedical Engineering
Image Processing
Wavelet TransformKernel (Windows)
Laws Texture
Study of Classification of Lung Tumor Based on CT/PET Images Technique of studying image ( gray level) Training skill of machine learning
What’s Today?
Gray level studying DSP, Kernel( window)
Resolution of image 4000*3000, 1024*768, 640*480, 320*240
How about in Biomedical Image?
Why Image Processing?
The Biomedical Image TodayCT:
512*512PET:
128*128
Why Image Processing?
Why Image Processing?
Brain v.s. Lung Tumors
Introduction and Back ground Technique Experiments Discussion and Conclusion
Outline
Introduction and Back ground Technique Experiments Discussion and Conclusion
Introduction
Lung Tumor High Death Ratio Nerve-less
Introduction
Introduction
Image Load
Pre-processin
g
Co-Registratio
n
ROIFeature Extraction
Classification
Co-Registrat
ion
Feature Extracti
on
Down / Up sampling ; Wavelet Transform
Wavelet ; Laws Texture ; Other Methods
Wavelet Transform:
Introduction--Wavelet Transform
J. J. Ding, 09 月 15 日上課資料 , P 43
Introduction--Wavelet Transform
Ivan W. Selesnick, Wavelet Transforms, 2007
Introduction--Wavelet Transform
Introduction
Ivan W. Selesnick, Wavelet Transforms, 2007
(2 ) ( ) ( )y n c n d n (2 1) ( ) ( )y n c n d n
Introduction--Wavelet Transform
Ivan W. Selesnick, Wavelet Transforms, 2007
Introduction--Wavelet Transform
Ivan W. Selesnick, W
avelet Transforms, 2007
Wavelet Transform: Improvement???
Haar !!
Introduction--Wavelet Transform
Haar Transform:
Introduction--Wavelet Transform
Introduction--Wavelet Transform
Wavelet Transform Haar Transform
Wavelet Transform:
Introduction--Wavelet Transform
J. J. Ding, 09 月 15 日上課資料 , P 46
Introduction--Wavelet Transform
Laws features The texture energy measures developed
by Kenneth Ivan Laws at the University of Southern California have been used for many diverse applications. These measures are computed by first applying small convolution kernels to a digital image, and then performing a nonlinear windowing operation.
Introduction—Laws Texture
http://www.ccs3.lanl.gov/~kelly/ZTRANSITION/notebook/laws.shtml
Laws features 3 element kernel 5 element kernel High order kernel
Introduction—Laws Texture
M.T. Suzuki, Y. Yaginuma, H. Kodama, A Texture Energy Measurement Technique for 3D Volumetric Data, 2009 IEEE International Conference on Systemshttp://www.ccs3.lanl.gov/~kelly/ZTRANSITION/notebook/laws.shtml
Laws features 3 element kernel
Level: [1 2 1];Edge: [-1 0 1];Spot: [-1 2 -1];
Introduction—Laws Texture
M.T. Suzuki, Y. Yaginuma, H. Kodama, A Texture Energy Measurement Technique for 3D Volumetric Data, 2009 IEEE International Conference on Systemshttp://www.ccs3.lanl.gov/~kelly/ZTRANSITION/notebook/laws.shtml
Laws features
Introduction—Laws Texture
Laws features 5 element kernel
L5 = [1, 4, 6, 4, 1]; E5 = [−1,−2, 0, 2, 1]; S5 = [−1, 0, 2, 0,−1]; R5 = [1,−4, 6,−4, 1]; % ripple W5 = [−1, 2, 0,−2, 1]; % wave
Introduction—Laws Texture
M.T. Suzuki, Y. Yaginuma, H. Kodama, A Texture Energy Measurement Technique for 3D Volumetric Data, 2009 IEEE International Conference on Systemshttp://www.ccs3.lanl.gov/~kelly/ZTRANSITION/notebook/laws.shtml
Laws features
Introduction—Laws Texture
Laws features Image processing --- 2D case
L5L5 L5E5 L5S5 L5R5 L5W5E5L5 E5E5 E5S5 E5R5 E5W5S5L5 S5E5 S5S5 S5R5 S5W5R5L5 R5E5 R5S5 R5R5 R5W5W5L5 W5E5 W5S5 W5R5 W5W5
Introduction—Laws Texture
M.T. Suzuki, Y. Yaginuma, H. Kodama, A Texture Energy Measurement Technique for 3D Volumetric Data, 2009 IEEE International Conference on Systemshttp://www.ccs3.lanl.gov/~kelly/ZTRANSITION/notebook/laws.shtml
Laws features
Introduction—Laws Texture
Introduction—Laws Texture
L5L5
E5E5
S5S5
R5R5
W5W5
CT - computed tomography PET - Positron emission
tomography
Introduction-- Background
CT - Computed Tomography Digital geometry processing is 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 .
http://translate.google.com/translate?hl=zh-TW&langpair=en|zh-TW&u=http://en.wikipedia.org/wiki/X-ray_computed_tomography
Introduction-- Background
PET - Positron Emission Tomography A nuclear medicine imaging technique that produces a three-dimensional image or
picture of functional processes in the body. The system detects pairs of gamma rays emitted indirectly by a positron-emitting radionuclide (tracer), which is introduced into the body on a biologically active molecule. Three-dimensional images of tracer concentration within the body are then constructed by computer analysis. In modern scanners, three dimensional imaging is often accomplished with the aid of a CT X-ray scan performed on the patient during the same session, in the same machine.
If the biologically active molecule chosen for PET is FDG, an analogue of glucose, the concentrations of tracer imaged then give tissue metabolic activity, in terms of regional glucose uptake. Although use of this tracer results in the most common type of PET scan, other tracer molecules are used in PET to image the tissue concentration of many other types of
Introduction-- Background
http://en.wikipedia.org/wiki/Positron_Emission_Tomography
PET - Positron emission tomography FDG ( Fludeoxyglucose) :
氟代脱氧葡萄糖
Introduction-- Background
http://en.wikipedia.org/wiki/Positron_Emission_Tomography
Background
CT V.S. PET
Introduction and Back ground Technique Experiments Discussion and Conclusion
Technique
Feature Extracting – 1 (on CT) Down sampling (for co-registry) Overlap CT/PET( Down/Up Sampling) Feature Extracting – 2 (on PET) Machine Learning
Technique
Background
CT V.S. PET
Feature Extracting – 1 (on CT) Volume Rectangular Fit Histogram featuresLaws featuresWavelet
: : :
Technique –Feature Extracting – 1 (on CT)
Technique –Feature Extracting – 1 (Wavelet)
M
i
N
j
jiIMxN
Energy1 1
2 ),(1
2D Case
)),(
log(),(1
2
2
1 12
2
normjiI
normjiI
MxNEntropy
M
i
N
j
Technique –Feature Extracting – 1 (Wavelet)
3D Case
M
i
N
j
L
k
kjiIMxNxL
Energy1 1
2
1
),,(1
)),,(log()),,((12
2
1 1 12
2
normkjiI
normkjiI
MxNxLEntropy
M
i
N
j
L
k
Technique –Feature Extracting – 1 (Laws Texture)
M
i
N
j
jiIMxN
Energy1 1
2 ),(1
2D Case
Technique –Feature Extracting – 1 (Laws Texture)
M
i
N
j
jiIMxN
Energy1 1
2 ),(1
3D Case
Down sampling (for co-registry)
Technique –Down sampling (for co-registry)
Raw Image
Low Pass(Average)
High Pass 1(X direction)
High Pass 2(Y direction)
High Pass 3(Corner)
Down sampling (for co-registry)
Technique –Down sampling (for co-registry)
Raw Image
Low Pass(Average)
High Pass 1(X direction)
High Pass 2(Y direction)
High Pass 3(Corner)
Down-samples Image
Feature Extracting – 2 (on PET) SUV Leveled SUV Largest Region’s SUV Other probability features
Technique –Feature Extracting – 2 (on PET)
Feature Extracting – 2 (on PET)
Technique –Feature Extracting – 2 (on PET)
PAWITRA MASA-AH, SOMPHOB SOONGSATHITANON, A novel Standardized Uptake Value (SUV) calculation of PET DICOM files using MATLAB, NEW ASPECTS OF APPLIED INFORMATICS, BIOMEDICAL ELECTRONICS & COMMUNICATIONS
Feature Extracting – 2 (on PET)
Technique –Feature Extracting – 2 (on PET)
Tumor
Level 1
Sub SUV
Level 2
Sub SUV
Level 3
Sub SUV
Level 4
Sub SUV
Level 5
Sub SUV
Feature Feature Feature Feature Feature
Machine Learning Logistic Neural Network SVM (Support Vector Machine) J48
Technique –Machine Learning
Introduction Background Technique Experiments Discussion and Conclusion
Experiments
Sorry, they are now in America
Experiments
Introduction Background Technique Experiments Discussion and Conclusion
Discussion and Conclusion
Discussion: Relation between Image Processing,
DSP, and TWD Kernel of Image Processing Development of Each kernel
Discussion and Conclusion
Relation between Image Processing, DSP, and TWD TWD:
Analyzing signal with mathematically way, either enhancement of complexity of equation and reducing the amount of computation.
DSP: Dealing the signal with discrete time work.
DIP: Take advantage of these two to give us more
probabilities on studying images.
Discussion and Conclusion
Kernel of Image Processing Similar to the window function on short
time signal analysis Either Gaussian filter (low pass filtering,
averaging) and edge detection (high pass filtering) are applied to turn into features
Discussion and Conclusion
Development of Each kernel Low pass filter High pass filter
Discussion and Conclusion
Development of Each kernel Low pass filter
Down sample ( average) [1 1]
Laws texture (level) [1 2 1], [1 4 6 4 1]
Gaussian blur (normal distribution) [1 8 12 16 12 8 1]
Discussion and Conclusion
Development of Each kernel High pass filter
Down sample ( change) [1 -1]
Laws texture (edge, ripple) [-1 -2 0 2 1], [1,−4, 6,−4, 1]
Gaussian Laplace Filter Subtract by two Gaussian filter with same
mean, different STD.
Discussion and Conclusion
Development of Each kernel High pass filter
Down sample ( change) [1 -1]
Laws texture (edge, ripple) [-1 -2 0 2 1], [1,−4, 6,−4, 1]
Gaussian Laplace Filter Subtract by two Gaussian filter with same
mean, different STD.
Discussion and Conclusion
Development of Each kernel High pass filter
Discussion and Conclusion
Development of Each kernel High pass filter
Discussion and Conclusion
Conclusion: Image processing is right an example
which implement DSP and TWD. Texture Feature give doctors more clues for
diagnosing More kinds of kernel provide more feature
for machine learning.
Discussion and Conclusion