Federica Caselli Department of Civil Engineering University of Rome Tor Vergata Corso di Modellazione e Simulazione di Sistemi Fisiologici Medical Imaging XRay CT ID: 529525
Download Presentation The PPT/PDF document "Medical Image Processing" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
Medical Image Processing
Federica Caselli
Department of Civil Engineering University
of Rome Tor
Vergata
Corso
di
Modellazione
e
Simulazione
di
Sistemi
FisiologiciSlide2
Medical Imaging
X-Ray
CT
PET/SPECT
Ultrasound
MRI
Digital Imaging!Slide3
Medical Image
Processing
Image compression
Image
denoisingImage enhancement
Image segmentationImage registrationImage fusion
What
kind
?
What
for
?
Image storage, retrieval, transmission
Telemedicine
Quantitative analysis
Computer aided diagnosis, surgery, treatment and follow up
To
name
but a
few!
Image analysis software are becoming an essential component of the medical instrumentationSlide4
Two examples
Mammographic
images
enhancement and denoising for
breast cancer diagnosis
Delineation
of
target volume
for
radiotheraphy
in
SPECT/PET
imagesSlide5
Mammographic image enhancement
MASSES
Disease
signs
in
mammograms
:
Shape
Boundary
EARLY DIAGNOSIS IS CRUCIAL FOR IMPROVING PROGNOSIS!Slide6
Mammographic image enhancement
EARLY DIAGNOSIS IS CRUCIAL FOR IMPROVING PROGNOSIS!
Morphology
,
size
(0.1
- 1 mm),
number
and
clusters
In
60-80
%
of
breast
cancers
at
hystological
examination
MICROCALCIFICATIONS
INTERPRETING MAMMOGRAMS IS AN EXTREMELY COMPLEX TASK
Disease
signs
in
mammograms
:Slide7
Transformed-domain processing
T
1)
Transform
Transformed
domain
representation
Image
T
-1
3)
Inverse
Transform
Enhanced
image
2)
Transformed-domain
processing
Modified
image
in
transformed
domain
E(x)
Transformed-domain processing
: signal is processed in a “suitable” domain. “Suitable” depends on the applicationSlide8
Fourier-based processing
S + N
S: 200 Hz
N: 5000 Hz
|X
(
ω
)|
LPF
|H
(
ω
)|
|Y
(
ω
)|
Is it suitable for mammographic image processing?Slide9
Fourier-based processing
?
Fourier is extremely powerful for stationary signals but
No time (or space) localizationSlide10
Short-Time Fourier Transform
Frequency and time domain information!
However a compromise is necessary...Slide11
Short-Time Fourier TransformSlide12
Short-Time Fourier Transform
Narrow window
Time
FrequencySlide13
Time
Frequency
Short-Time Fourier Transform
Medium windowSlide14
Time
Frequency
Short-Time Fourier Transform
Large window
Once chosen
the
window, time and frequency resolution are fixed
Wavelet Transform:
more windows
, with
suitable
time and frequency resolution!Slide15
Wavelet Transform
“If you painted a picture with a sky, clouds, trees, and flowers, you would use a different size brush depending on the size of the features. Wavelet are like those brushes.”
I. Daubechies
u
sSlide16
Wavelet Transform
“If you painted a picture with a sky, clouds, trees, and flowers, you would use a different size brush depending on the size of the features. Wavelet are like those brushes.”
I. DaubechiesSlide17
Wavelet Transform
“If you painted a picture with a sky, clouds, trees, and flowers, you would use a different size brush depending on the size of the features. Wavelet are like those brushes.”
I. DaubechiesSlide18
Wavelet Transform
“If you painted a picture with a sky, clouds, trees, and flowers, you would use a different size brush depending on the size of the features. Wavelet are like those brushes.”
I. DaubechiesSlide19
Wavelet Transform
“If you painted a picture with a sky, clouds, trees, and flowers, you would use a different size brush depending on the size of the features. Wavelet are like those brushes.”
I. DaubechiesSlide20
Wavelet Transform
I. Daubechies
“If you painted a picture with a sky, clouds, trees, and flowers, you would use a different size brush depending on the size of the features. Wavelet are like those brushes.”
Many type of Wavelet Transform (WT):
Continuous
WT and
Discrete
WT, each with several choices for the mother wavelet.
Moreover,
Discrete-Time
Wavelet Transform are needed for discrete signalsSlide21
Dyadic Wavelet Transform
S.
Mallat
and S.
Zhong
, “
Characterization
of
signals
from
multiscale
edge
”,
IEEE
Transactions
on Pattern
Analysis
and
Machine Intelligence
, Vol. 14, No. 7, 1992. Slide22
Implementation
Decomposition
Discrete-time
transform
Algorithme à trous
Higher
scales
G
(2
)
H
(2
)
d
2
a
2
a
o
G
(
)
H
(
)
d
1
a
1
G
(4
)
H
(4
)
a
3
d
3Slide23
Implementation
G
(
)
H
(
)
G
(2
)
H
(2
)
G
(4
)
H
(4
)
Decomposition
a
o
d
1
a
1
d
2
a
2
K
(4
)
H
(4
)
K
(
)
H
(
)
K
(2
)
H
(2
)
Reconstruction
a
2
a
1
a
o
Algorithme à trous
d
3
a
3
Higher
scales
Discrete-time
transformSlide24
Filters
G
Gradient
filterr = 1Slide25
Filters
G
Laplacian
filterr = 2Slide26
1D Transform
GRADIENTE
LAPLACIANO
Signal
Detail
coefficients
ScaleSlide27
Denoising
W
W
-1
outlier
Segnale rumoroso
Segnale ricostruitoSlide28
Wavelet
Thresholding
Hard
thresholding
Soft
thresholding
Key
issue
:
thresholds
selectionSlide29
d
v
1
G
(
y
)
G
(
x
)
H
(
x
)
H
(
y
)
G
(2
y
)
G
(2
x
)
H
(2
x
)
H
(2
y
)
H
(2
x
)
H
(2
y
)
L
(2
x
)
K
(2
y
)
K
(2
x
)
L
(2
y
)
H
(
x
)
H
(
y
)
L
(
x
)
K
(
y
)
K
(
x
)
L
(
y
)
Decomposition
Reconstruction
a
o
a
o
d
o
1
d
v
2
d
o
2
a
1
a
2
a
1
Algorithme à trous
Implementation
Discrete-time
transformSlide30
2D TransformSlide31
2D TransformSlide32
DDSM
5491 x 2761
12 bpp
Resolution
:
43.5
m
* University of South Florida,
http://marathon.csee.usf.edu/Mammography/Database.html
ROI 1024 x 1024
4.45 cmSlide33
Masses
2
1
3
4
d
v
d
o
m
ScaleSlide34
Microcalcifications
2
1
3
4
d
v
d
o
mSlide35
W
1)
Decomposition
W
avelet
coefficients
Image
W
-1
3)
Reconstruction
Enhanced
image
Enhancing
vertical
features
Linear
enhancement
Varying
the
gain
G=8
G=20
2)
Enhancement
Modified
coefficients
E(x)
Extremely
simple
and
powerful
tool
for
signal
prosessing
.
Many
many
applications
!
Wavelet-based
signal processingSlide36
Wavelet-based
signal processing
Key
issue
: operator and
thresholds selection
Mammograms
have
low
contrast
Must
be
adaptive
and
automatic
G
E(x)
Saturation
region
Risk
region
T1
Amplification
region
T2