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Medical Image Processing
Medical Image Processing

Medical Image Processing - Description


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

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wavelet transform image time transform wavelet time image size processing fourier domain features trees frequency painted picture sky clouds brush flowers depending

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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

Shom More....
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