Digital image selfadaptive acquisition in medical xray imaging

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Digital image self-adaptive acquisition in
medical x-ray imaging
Bao Jie, Gao Jun et.al. Lab on Image Information
Processing Hefei University of Technology , China
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Content

• What is X-ray fluoroscopy system and digital
  acquisition system

The principle and implementation of
self-adaptive digital acquisition
Experiment and Conclusions
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1. What is X-ray fluoroscopy system and digital
acquisition system?
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Whats X-ray fluoroscopy system?

• X-ray fluoroscopy system is a system for medical
  diagnosing that can render image of the body of
  patient by convert X-ray which pass through and
  attenuated by the body into visible light and
  record it on film or other media. Its a very
  common method for examination in hospitals.

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Construction of X-ray fluoroscopy system
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Why study the digital acquisition of X-ray
fluoroscopy system?(1)

• The digitalization of x-ray imaging is very
  important for PACS (Picture Archiving and
  Communication system) high-quality digital X-ray
  medical images are indispensable for PACS data
  source.

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Why study the digital acquisition of X-ray
fluoroscopy system?(2)

• There are three ways to digitalize x-ray imaging
• Computed Radiography (CR)
• Digital Radiography (DR)
• Video digital acquisition.
• Advantages of video digital acquisition ability
  to see dynamic change of organs, device
  simplicity, operating convenience, and low-cost

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The main difficulties in video digital
acquisition

• x-ray fluoroscopy image detection noise and
  digital quantum noise

Adjusting imaging contrast and resolution
Device background signal
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How to deal with them?
choose a appropriate working point automatically
and suppressed background signal by software

• Improve hardware quality of x-ray imaging system

Choose grabber board with high quantization
precision
Voltage stabilization and electromagnetic
shielding
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Digital video processing system(1)
Enhancement
Annotation
Display
Diagnose

manual
Information

navigation
x-ray video
Host
NSP
grabber board
Report
Aided
diagnose
Aided
treat
ment
Control
Archiving and backup
Query and management
PACS
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Digital video processing system(2)
Host should analyze the input signal while
sampling and quantization to adjust grabber board
setting for valid signal to utilize the dynamic
range sufficiently, and to make device working in
linear range. The grabber board we used is NSP
(Native Signal Process) frame-grabber board
DT3153-LS, it can adjust reference, offset, gain,
black level and white level by software, which
make it possible for self-adaptive acquisition by
software.
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2. The principle and implementation of
self-adaptive digital acquisition
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Self-adaptive digital acquisition

• To resolve problems brought forward in section
  1, we use digital subtraction technique to
  realize background removing for self-adaptive
  acquisition, and monitor the dynamic range of
  image valid region to search for the best
  acquisition working point automatically.

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Self-adaptive digital acquisition system

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3.1Valid region recognition

• The acquired image is not entirely valid.
  Generally speaking, the valid region is a circle.
  
• We should only count on valid region while
  removing background and analyzing the image
  feature to adjust acquisition parameters, so we
  must recognize the valid region at first.

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Valid observe region
(a) Whole valid observe region. White line is
detected region edge by improved seed algorithm.
(b) Valid observe region with occlusion
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Valid region detection algorithm (1)

• 1.Compute the histogram of left and right narrow
  edges of the image, the gray-level corresponding
  to histogram peak value is the gray-level of
  invalid region.
• 2. Perform median filtering to remove noise.
• 3. Grow region using classical seed growing
  algorithm starting from any invalid point.

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Valid region detection algorithm (2)

• 4. Generate initial mask(bilevel ) image of valid
  region. Perform Sobel operator to this image to
  extract its edge.
• 5. Detect circle by general Hough transform get
  the radius and the center of the circle.
• 6. Generate valid region mask using result of
  step 5.

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3.2 Background removing

• Nonuniform background will affect image quality
  and the computing of image characteristic to
  adjust acquisition parameters.
• So a digital subtraction will remove background
  signal while keep the validity of information.

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Background removing algorithm

• Acquire and save device background signal (I1)
  when device is idle.
• Acquire images to be observed (I2).
• Perform image operation in valid region
  I3I1-I2 I4NOT I3
• I4 is the image signal removed of background.

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3.3 Setting acquisition working point

• After above-mentioned pre-processing, we will
  adjust black level, white level, gain, reference
  and offset automatically based on histogram
  analysis of image valid region to obtain best
  acquisition quality.
• Black level - offset
• White level reference / gain -offset

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Meaning of offset, gain and reference
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Meaning of black level and white level
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Working point setting rule

• Decreasing offset will shift image to light zone,
  increasing offset will shift image to dark zone,
  namely offset behaves as brightness adjusting
  decreasing reference will compress image to light
  zone, increasing reference will compress image to
  dark zone, namely reference behaves as contrast
  adjusting.

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Dynamic range analysis of valid region

• Analyze the proportion of dark zone and light
  zone in the histogram of image valid region, the
  aim of adjusting is to keep proper proportion of
  dark zone and light zone for best image
  acquisition performance.
• Setting brightness at first to ensure dark zone
  isn't too much then setting contrast( that is,
  properly setting white level by adjusting
  reference).

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Self-adaptive acquisition parameters setting
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Universal acquisition parameters choosing

• It's very inefficient and unnecessary to setting
  best working point every time we take
  fluoroscopy.
• In practice, expert judgment and adjusting is
  used to choose universal acquisition parameters.

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3. Experiment and Conclusions
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Run interface of self-adaptive acquisition module

• Run interface of self-adaptive acquisition module
  in ImagePro implemented by Visual C6.0

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Valid region detection
Original Image
Valid region mask image
Sobel edge-detect image
integrated valid region
Rim of Valid Region by improved algorithm
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Background removing
acquired image with nonuniform background
device background signal
image after removing device background
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self-adaptive adjusting(1)

• Acquired image before self-adaptive adjusting.
  Black level0V, white level 0.7V, offset0V,
  gain1, reference 0.7V

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self-adaptive adjusting(2)

• Histogram of valid region in (1). mean 70.48,
  median value54. Image is too dark.

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self-adaptive adjusting(3)

• Acquired image after self-adaptive adjusting.
  Black level -0.042V, white level 0.258V,
  offset0.042V, gain2, reference 0.6V

scapula
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self-adaptive adjusting(4)

• Histogram of valid region in (3). mean
  121.19,median value 113.

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Conclusions

• It's possible to implement self-adaptive
  acquisition of medical video image automatically
  by integrating various images processing method.
  The proposed method has recognized the valid
  region of image and removed the background, then
  adjusted acquisition parameters by analyzing
  image dynamic range to obtain best acquisition
  quality. But there still some problem remained to
  be resolved.

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Thank you!