Medical Image Analaysis
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Transcript of Medical Image Analaysis
Medical Image Analaysis
Atam P. Dhawan
Image Enhancement: Spatial Domain
Histogram Modification
1-L0,1,...,ifor)( ii nrh
where ir is the ith gray-level in the image for a total of L gray values and in is the
number of occurrences of gray-level ir in the image.
n
nrp i
i )(
Medical Images and Histograms
Histogram Equalization
1-L0,1,...,ifor
)()(
0
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rprTs
Image Averaging Masks
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ii yxg
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f(-1,0)
f(0,-1) f(0,0) f(0,1)
f(1,0)
f(-1,-1) f(-1,0) f(-1,0)
f(0,-1) f(0,0) f(0,1)
f(0,-1) f(1,0) f(1,1)
Image Averaging
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Median Filter
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Laplacian: Second Order Gradient for Edge Detection
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Image Sharpening with Laplacian
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Feature Adaptive Neighborhood
Xc Xc
Center Region
Surround Region
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)3(),()3( cc xyxfx
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Feature Enhancement
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scs
C’(x,y)=F{C(x,y)}
Micro-calcification Enhancement
Frequency-Domain Methods
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Low-Pass Filtering
High Pass Filtering
Wavelet Transform
Fourier Transform only provides frequency information.
Windowed Fourier Transform can provide time-frequency localization limited by the window size.
Wavelet Transform is a method for complete time-frequency localization for signal analysis and characterization.
Wavelet Transform..
Wavelet Transform : works like a microscope focusing on finer time resolution as the scale becomes small to see how the impulse gets better localized at higher frequency permitting a local characterization
Provides Orthonormal bases while STFT does not.
Provides a multi-resolution signal analysis approach.
Wavelet Transform…
Using scales and shifts of a prototype wavelet, a linear expansion of a signal is obtained.
Lower frequencies, where the bandwidth is narrow (corresponding to a longer basis function) are sampled with a large time step.
Higher frequencies corresponding to a short basis function are sampled with a smaller time step.
Continuous Wavelet Transform
Shifting and scaling of a prototype wavelet function can provide both time and frequency localization.
Let us define a real bandpass filter with impulse response (t) and zero mean:
This function now has changing time-frequency tiles because of scaling. a<1: (a,b) will be short and of high frequency a>1: (a,b) will be long and of low frequency
a
bt
at
tft
dttfa
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as defined is (CWT) Transform Wavelet contnuousA
0)0()(
,
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Wavelet Decomposition
T h e w a v e l e t t r a n s f o r m o f a s i g n a l i s i t s d e c o m p o s i t i o n o n a f a m i l y o fr e a l o r t h o n o r m a l b a s e s
m n ( x ) o b t a i n e d t h r o u g h t r a n s l a t i o n a n dd i l a t i o n o f a k e r n e l f u n c t i o n ( x ) k n o w n a s t h e m o t h e r w a v e l e t .
W h e r e m , n Z , a s e t o f i n t e g e r s
)2(2)( 2/, nxx mm
nm
Wavelet Coefficients
Using orthonormal property of the basis functions, wavelet coefficients of a signal f(x) can be computed as
The signal can be reconstructed from the coefficients as
)()()( ,, xdxxfd nmnm
)()( ,, xdxf nmm n
nm
Wavelet Transform with Filters The mother wavelet can be constructed using a scaling
function (x) which satisfies the two-scale equation
Coefficients h(k) have to meet several conditions for the set of basis functions to be unique, orthonormal and have a certain degree of regularity.
For filtering operations, h(k) and g(k) coefficients can be used as the impulse responses correspond to the low and high pass operations.
)()1()(
)2()(2)(
)2()(2)(
klk
where
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Decomposition
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G
H
G
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2 2
2
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Data
Wavelet Decomposition Space
V0 data
V1 W1
V2 W2
V3 W3
Image Decomposition
h g
sub-sample
Level 0 Level 1
h- h
h-g
g-h
g-g
horizontally vertically
sub-sample
g
gh
h
XImage
Wavelet and Scaling Functions
Image Processing and Enhancement
Image Segmentation
Edge-Based Segmentation Gray-level Thresholding Pixel Clustering Region Growing and Spiliting Artificial Neural Network Model-Based Estimation
Gray-Level Thesholding
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Region Growing
Center Pixel
Pixels satisfying the
similarity criterion
Pixels not satisfying the similarity criterion
3x3 neighborhood
5x5 neighborhood
7x7 neighborhood
Segmented region
Neural Network Element
x 1
x n
x 2
1
N o n - L i n e a r A c t i v a t i o n F u n c t i o n F
n
inii wxwFy
11
w n + 1
w 1
w 2
w n
n
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Artificial Neural Network: Backpropagation
Hidden Layer Neurons
Output Layer Neurons
Ly1
x1 x2 x3 xn 1
Ly2 Lny
RBF Network
RBF Unit 1
RBF Unit 2
RBF Unit n
Input ImageSliding
Image Window
Output
Linear Combiner
RBF Layer
RBF NN Based Segmentation
Image Representation
Bottom-Up
Scenario
Scene-1 Scene-I
Object-1 Object-J
S-Region-1 S-Region-K
Region-1 Region-L
Pixel (i,j)
Edge-MEdge-1
Pixel (k,l)
Top-Down
Image Analysis: Feature Extraction Statistical Features
Histogram Moments Energy Entropy Contrast Edges
Shape Features Boundary encoding Moments Hough Transform Region Representation Morphological Features
Texture Features Spatio Frequency Features Relational Features
Image Classification
Feature Based Pattern Classifiers Statistical Pattern Recognition
Unsupervised Learning Supervised Learning
Sytntactical Pattern Recognition Logical predicates
Rule-Based Classifers Model-Based Classifiers Artificial Neural Networks
Morphological Features
A
B
BA
BA
Some Shape Features
A
EH
D
B
C
FG
O
•Longest axis GE.•Shortest axis HF.•Perimeter and area of the minimum bounded rectangle ABCD.•Elongation ratio: GE/HF•Perimeter p and area A of the segmented region.
•Circularity
•Compactness2
4
p
AC
A
pC p
2
Relational Features
A
C
B
D
F
I
E
B
C
A
I
ED
F
Nearest Neighbor ClassifierA d i s t a n c e m e a s u r e )( fjD i s d e f i n e d b y t h e E u c l i d e a n d i s t a n c e i n t h e f e a t u r e s p a c e a s
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w h e r e CjN
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i s t h e m e a n o f t h e f e a t u r e v e c t o r s f o r t h e c l a s s jc a n d N j i s t h e t o t a l n u m b e r o f f e a t u r e
v e c t o r s i n t h e c l a s s jc .
T h e u n k n o w n f e a t u r e v e c t o r i s a s s i g n e d t o t h e c l a s s ic i f
)]([min)( 1 ff jC
ji DD
Rule Based Systems
Strategy RulesA priori knowledge
or models
Focus of Attention Rules
Knowledge Rules
Activity
Center
InputDatabase
OutputDatabase
Strategy RulesStrategy Rule SR1:
If NONE REGION is ACTIVE NONE REGION is ANALYZED Then ACTIVATE FOCUS in SPINAL_CORD AREA Strategy Rule SR2:
If ANALYZED REGION is in SPINAL_CORD AREA ALL REGIONS in SPINAL_CORD AREA are NOT ANALYZED Then ACTIVATE FOCUS in SPINAL_CORD AREA Strategy Rule SR3:
If ALL REGIONS in SPINAL_CORD AREA are ANALYZED ALL REGION in LEFT_LUNG AREA are NOT ANALYZED
Then ACTIVATE FOCUS in LEFT_LUNG AREA
FOA Rules
Focus of Attention Rule FR1:
If REGION-X is in FOCUS AREA REGION-X is LARGEST REGION-X is NOT ANALYZED
Then ACTIVATE REGION-X
Focus of Attention Rule FR2:
If REGION-X is in ACTIVE MODEL is NOT ACTIVE
Then ACTIVATE KNOWLEDGE_MERGE rules
Knowledge Rules
Knowledge Rule: Merge_Region_KR1 If
REGION-1 is SMALL REGION-1 has GIGH ADJACENCY with REGION-2 DIFFERENCE between AVERAGE VALUE of REGION-1 and
REGION-2 is LOW or VERY LOW REGION-2 is LARGE or VERY LARGE
Then MERGE REGION-1 in REGION-2 PUT_STATUS ANALYZED in REGION-1 and REGION-2
Neuro-Fuzzy Classifiers
M1
winner-take-alloutput layer
L
1
fuzzy membershipfunction layer
x1
xi
xd
hyperplanelayer
inputlayer
max
M2
MK
C
Extraction of Ventricles
Composite 3D Ventricle Model
Extraction of Lesions
Extraction of Sulci
Segmented Regions
Center for Intelligent Vision System
Structural Signatures: Volume Measurements of Ventricular Size and Cortical Atrophy in Alcoholic and Normal Populations from MRI
Ventricular Volume Alcoholics
Ventricular Volume Normal
Sulcus Volume Alcoholics
Sulcus Volume Normal
0 0.05 0.1 0.15 0.2 0.25
Multi-Parameter Measurements
Do = f{T1, T2, HD, T1+Gd, pMRI, MRA, 1H-MRS, ADC, MTC, BOLD}where,
T1 = NMR spin-lattice relaxation timeT2 = NMR spin-spin relaxation timeHD = Proton densityGd+T1 = Gadolinium enhanced T1
pMRI = Dynamic T2* images during Gd bolus injectionMRA = Time of flight MR angiographyMRS = Magnetic Resonance SpectroscopyADC= Apparent Diffusion CoefficientMTC= Magnetization Transfer ContrastBOLD = Blood Oxygenation Level Dependent
Regional Classification & Characterization
1. White matter 2. Corpus callosum 3. Superficial gray
4. Caudate 5. Thalamus 6. Putamen
7. Globus pallidus 8. Internal capsule 9. Blood vessel
10. Ventricle 11. Choroid plexus 12. Septum pellucidium
13. Fornices 14. Extraaxial fluid 15. Zona granularis
16. Undefined
Adaptive Multi-Level Multi-Dimensional Analysis
Database ofTissue Signatures
Selection of classesand cluster analysis
New classformation
SignatureSelection
Markov RandomField
BasedClassification
Adaption tospatial domain
Evaluate statisticaldistribution of classes
and probabilities
AcceptableNo more classes
Pixels withlow prob (classified)
Relax class selectioncriteria
No
Yes
Yes
No
Not
Acceptable
All pixels classified ?
Building Signatures
Analysis of 15 classes (normal group)
Stroke Effect on 12-Years Old Subject
Center for Intelligent Vision and Information System
Typical Function of Interest Analysis: Dhawan et al. (1992)Typical Function of Interest Analysis: Dhawan et al. (1992)
FVOI Signature
Anatomical Reference
(S.C.A.)
Functional Reference
(F.C.A.)
ReferenceSignatures
MR Image(New Subject)
PET Image(New Subject)
MR-PETRegistration
Principal Axes Registration
= 1 if (x,y,z) is in the object = 0 if (x,y,z) is not in the objectB x y z( , , )
x
xB x y z
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Binary Volume
Centroids
PAR
1. Translate the centroid of V1 to the origin. 2. Rotate the principal axes of V1 to coincide
with the x, y and z axes. 3. Rotate the x, y and z axes to coincide with
the principal axes of V2. 4. Translate the origin to the centroid of V2. 5. Scale V2 volume to match V1 volume.
Iterative PAR for MR-PET Images(Dhawan et al, 1992)
1. Threshold the PET data.
2. Extract binary cerebrum and cerebellum areas from MR scans.
3. Obtain a three-dimensional representation for both MR and PET data: rescale and interpolate. 4. Construct a parallelepiped from the slices of the interpolated PET data that contains the binary PET brain volume. This volume will be referred to as the "FOV box" of the PET data. 5. Compute the centroid and principal axes of the binary PET brain volume.
Iterative PAR…
6. Add n slices to the FOV box on the top and the bottom such that the augmented FOV(n) box will have the same number of slices as the binary MR brain. Gradually shrink this FOV(n) box back to its original size, FOV(0) box, recomputing the centroid and principal axes of the trimmed binary MR brain at each step iteratively.
7. Interpolate the gray-level PET data (rescaled to match the MR data) to obtain the PET volume.
8. Transform the PET volume into the space of the original MR slices using the last set of MR and PET centroids and principal axes.. Extract from the PET volume the slices which match the original MR slices.
IPARIteration 1
Iteration 2
Iteration 3
Center for Intelligent Vision and Information Systems
Multi-Modality MR-PET Brain Image Image Multi-Modality MR-PET Brain Image Image RegistrationRegistration
Center for Intelligent Vision and Information Systems
Multi-Modality MR-PET Brain Image RegistrationMulti-Modality MR-PET Brain Image Registration
Center for Intelligent Vision and Information Systems
Multi-Modality MR-PET Brain Image RegistrationMulti-Modality MR-PET Brain Image Registration
MR Volume Signatures