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and. Digital Image Processing. D igital S ubtraction A ngiography. วัตถุประสงค์. อธิบายขบวนการประมวลผลภาพดิจิตอลได้ อธิบายวิธีการปรับคอนทราสของภาพดิจิตอลได้ อธิบายการทำงานและควบคุม window ของภาพรังสีดิจิตอลได้ อธิบายวิธีการทำ Subtraction ภาพด้วยวิธีต่างๆ ได้. 1. 2. LUT Curve.

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Digital Image Processing

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Digital Image Processing


Subtraction Angiography


  • อธิบายขบวนการประมวลผลภาพดิจิตอลได้

  • อธิบายวิธีการปรับคอนทราสของภาพดิจิตอลได้

  • อธิบายการทำงานและควบคุม window ของภาพรังสีดิจิตอลได้

  • อธิบายวิธีการทำ Subtraction ภาพด้วยวิธีต่างๆ ได้



LUT Curve


Selection of

Enhancing Visibility of Detail

Digital Subtraction Angiography


Computed radiography

The need for subtraction

Subtraction for improvement in conspicuity

Mask image

Live image



(original+ contrast media)

Mask image

Live image


Image processing with Java

ให้นักศึกษาใช้ โปรแกรมนี้ในการทำ Digital Subtraction

Subtraction methods

1. Depth

2. Energy

3. Time

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

Compton/Photoelectric decomposition

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

Advantage/ Disadvantage

Dual energy subtraction images

Soft-tissue removed

Bone removed

3. Hybrid subtraction

Temporal subtraction + Energy subtraction

Image processing

1. Spatial filtering

2. Pixel shifting operation

3. Temporal filtering

4. Intensity transformations

5. Window/Level techniques

6. Parametric imaging

Spatial filtering is a method of selectively enhancing or diminishing specific spatial frequency components in an image

1. Spatial filtering

Diagram of two-dimensional digital spatial filtering


Low-pass filtering

High-pass filtering

Median filtering

Digital filtering(Convolution)

Each pixel in the processed images is derived from a set of pixels in the original image as determined by the mask.

Low-pass digital spatial filtering(Smoothing)

High-pass digital spatial filtering (Edge enhancement)

Filtered images



(Edge enhancement)



Median filtering

Mask = Median value of the appropriate 9 pixels in the original image

Median filtering images

Digital chest radiograph with unwanted dot artifacts

After application of 3x1 medial filter to remove dots

2. Pixel shifting operation

  • Rotation

  • Translation

  • Magnification

  • Minification

Pixel registration to reduce motion artifacts

3. Temporal filtering

1. Time interval difference(TID)

2. Integration

3. Blurred mask temporal subtraction

4. Recursive filtering (real time methods)

Generalized temporal filtering diagram

3.1. Time -interval difference subtraction

3.2. Integration

Pre-contrast and post-contrast images are summated(integrated) to reduce noise

Single pre-contrast image

Single post-contrast images

8 pre-contrast image

8 post-contrast image

Image integration

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 radiation dose

2. Reduce motion artifacts

4. Intensity transformation

Use of image processing to correct the non linearity of film

Gamma correction curve

Gamma correction curves can be use to enhance or reduce contrast

Contrast enhancement

Contrast reduction

Original image

Histogram equalization

Original arterial DSA image of the kidney

After histogram equalization

Histogram equalization

Gray scale





center C


width W




5. Windows / Level Techniques

Gray scale




Window 1


Window 2



Double windows techniques

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

Acute Tubular Necrosis

Example of parametric imaging

1. Time to peak enhancement

2. Mean transit time

3. Maximum pixel attenuation

4. Integrated attenuation change

5. Local volume distribution

6.Quantitative imaging : Temporal processing

An idealized contrast enhancement curve or Indicator dilution curve

Example of calculation

1. Peak or Maximum enhancement

2. Time to maximum enhancement

3.Time to half maximum enhancement

4. Integrated enhancement(area under the curve)

5. Mean transit time

6. etc


A. Cardiac output

B. Regional blood flow

C. Cardiac ventricular ejection fraction

D. Quantitation of left to right shut

E. etc

Quantitative imaging

Gamma variate parameters of typical time-concentration curve

A comparison between cardiac output estimations using DR and standard thermodilution methods

DSA quantitation of vessel stenosis

Identifies the region of stenosis, and normal portion, then calculate the degree of narrowing

DSA of right coronary artery stenosis

Boundary detection

1.After location of aortic valve plane and apex, the computer constructs a ray passing through the center(x) of the LV

2. A series of rays emanating from the center are drawn by the computer

3. The density of pixel values is measured , the edge is determined at 50% of the maximum values

End-diastolicED contours are shown for different thresholds values(50% and 75%)

End-systolicES contours are shown for different thresholds values(50% and 75%)

The ejection fraction is computed using the 50% thresholds silhouettes


  • ให้ใช้ โปรแกรม ImageJ ซึ่งสามารถทำ Image processing ได้หลากหลายวิธี นำมาใช้เป็นเครื่องมือในการทำ subtraction โดยให้นักศึกษาเลือกภาพต้นฉบับของตนเอง(*.jpg)ขนาดไม่เกิน 500k ส่งให้อาจารย์ที่ web ของรายวิชา 437401 Medical imaging จากนั้นอาจารย์จะสร้างวัตถุแปลกปลอมในภาพนั้นและส่งกลับให้นักศึกษาเพื่อให้ นักศึกษาใช้โปรแกรม ในการสร้างภาพสิ่งแปลกปลอมนั้นและส่งกลับที่ web เดิม


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