digital image processing
DESCRIPTION
and. Digital Image Processing. D igital S ubtraction A ngiography. วัตถุประสงค์. อธิบายขบวนการประมวลผลภาพดิจิตอลได้ อธิบายวิธีการปรับคอนทราสของภาพดิจิตอลได้ อธิบายการทำงานและควบคุม window ของภาพรังสีดิจิตอลได้ อธิบายวิธีการทำ Subtraction ภาพด้วยวิธีต่างๆ ได้. 1. 2. LUT Curve. - PowerPoint PPT PresentationTRANSCRIPT
Digital Image Processing
Digital
Subtraction Angiography
and
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1
2
LUT Curve
Selection of Curve
Enhancing Visibility of Detail
Digital Subtraction Angiography
Digital Subtraction Angiography
DSA
Computed radiography
The need for subtraction
Subtraction for improvement in conspicuity
Mask image Live image Mask-Live(original) (original + contrast media)
Mask image Live image Live-Mask
Image processing with Java
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Subtraction methods1. Depth
2. Energy
3. Time
1. Temporal subtraction(Time-dependent)
Temporal subtraction
- 1. Pre contrast images (mask images)
- 2 . ( )Post contrast images live images
3 . Subtraction of mask from live images
2. Energy subtraction
- Energy dependence of x ray attenuation of difference tissue
Dual energy subtraction
Dual energy subtraction
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Advantage / Disadvantage
1. Provide selective cancellation
2. Fast , in millisecond, minimized motion interference
1. More complex
2. More sensitive to scatter radiation
3.Impossible to remove soft-tissue and bone simultaneously
Bone removed
- Soft tissue removed
Dual energy subtraction images
3. Hybrid subtraction
Temporal subtraction + Energ y subtraction
Image processing1. Spatial filtering
2. Pixel shifting operation
3. Temporal filtering
4. Intensity transformations
5. Window/Level techniques
6. Parametric imaging
1. Spatial filteringSpatial filtering is a method of selectively enhancing or dimi
nishing specific spatial frequency components in an image
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Digital filtering(Convo
lution)tttt ttttt tt ttt
pr ocessed i mages i s der i ved f r omt
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determined by the.
• MethodsLow-pass filteringHigh-pass filteringMedian filtering
Low-pass digital spatial filtering(Smoothing)
91
High-pass digital spatial filtering (Edge enhancement)
Filtered images
-Low passSmoothin
g
-High pass (Edge enhan
cement)
Original
Median filtering
Mask = Media n value of the
appropriate 9 pixels in the ori ginal image
Median filtering images
Digital chest radiog raph with unwanted
dot artifacts
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er to remove dots
2. Pixel shifting operation
• Rotation
• Translation
• Magnification
• Minification
Pixel registration to reduce moti on artifacts
3. Temporal filtering
1. Time interv al
difference(TID)
2 . Integratiot
3. Blurred mast tttttttt ttttttttttt
4. Recursive fittttttt (real
time methods)
ttttttttttt t empor al fi l t
er i ng di agr am
3.1. Time -interval difference subtraction
3.2. Integration
- -Pre contrast and post con trast images are summat
ed(integrated) to reducenoise
Image integration
Single pre-contrast image
Single post-contrast images
8 pre-contrast image
8 post-contrast image
3.3. Blurred mask temporal subtraction
For cardiac study : increase s/n for mask image and the
edge of cardiac will blurred
3.4. Recursive filtering (real time methods)
1. Reduce radiatio n dose 2. Reduce motion
artifacts
4. Intensity transformation
ttt tt ttttt tttttttttt tt tttt ect t he non l i near i t y of fi l m
Gamma correction curve
Gamma correction curves can be use to enhance or reduce contrast
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Contrastreductio
t
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Histogram equalization
Original arterialt tt tt ttt tt tt
e kidney
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255
Display
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Histogram equalization
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Display
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5. Windows / Level Techniques
1024
0
Window width W
Window center C
Gray scaledisplay
White
Black
Windowing
Double windows techniques
1024
0
Window 1
Gray scaledisplay
White
Black
WindowingWindow 2
6. Parametric imaging
• The algorithms for image processing that provide a final displayed image in which the value of each pixel is related to the attenuation or attenuation change at the particular point in the patient
Parametric(functional) imaging A cute T ubular
Necrosis
Example of parametric imaging1. Time to peak enhancement2. Mean transit time3. Maximum pixel attenuation4. Integrated attenuation change5. Local volume distribution
An idealized contrast enhancement curve tt ttttttttt tttttttt ttttt
6.Quantitative imaging : Temporal processing
Quantitative imaging Example of calculation
1. Peak or Maxi mumenhancement
2. Time to maximum enhancement
3. Time to half maximum enhancement
4. Integrated enhancement (areaundert he cur ve)
5. Mean transit time6. et c
ApplicationA. Cardiac outputB. Regional blood flowC. Cardiac ventricular ej
ection fractionD. Quantitation of left to
right shut E. etc
Gamma variate parameters of typical -time concent ration curve
A comparison between cardiac output estimations using D R and standard thermodilution methods
DSA quantitation of vessel stenosis
DSA of right coronary arte ry stenosis
Identifies the region of ste nosis, and normal portion,
then calculate the degree of narrowing
Boundary detection
1.After location of aortic valve pl
ane and apex , th e computer cons
tructs a ray pass ing through the
center(x) of theLV
2. A series of rays emanating from the center
are drawn by th e computer
3. The density of pixel values i
s measured , th e edge is deter
mined at 50% o f the maximum
values
-End diastolic ED contours
are shown for different thre
sholds values (5 0 % and
75%)
-End systolic ES contours ar
e shown for di fferent thresh
olds values(5 t0 7
5%)
The ejection fracti on is computed
using the 50% thr esholds silhouett
es
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บรรณาน�กรม1. Image processing program with Java 2. Digital Subtraction Angiography.
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