UNIT 2: Image Processing Basics Flashcards

1
Q

Analog to digital conversion

A

Step 1: Scanning
Step 2: Sampling
Step 3: Quantization
Step 4: Coding

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2
Q

Step 1: Scanning

A

• Field of View is divided into a matrix
• Each pixel is given a coordinate location or address

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3
Q

Step 2: Sampling

A

• Samples are taken of the data
• Sampling Frequency: number of samples taken per unit length
• Determined by the Pixel Pitch

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4
Q

Step 2 sampling: Pixel pitch

A

The distance between the center of two adjacent pixels

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5
Q

Step 2 sampling: Nyquist Theorum

A

The sampling frequency must be at least twice the highest frequency of the sine wave

• Ex: A wave with a frequency of 5 Hz MUST have at least 10 samples taken by the ADC.

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6
Q

Step 2 sampling: Moire Pattern

A

-Sampling error
-If the sampling rates are too low, specific artifacts occur called moire patterns

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7
Q

T or F: the more samples the better

A

True

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8
Q

Step 3: Quantization

A

a gray shade is assigned to each individual image pixel

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9
Q

Step 3 Quantization: Bit depth

A

-Range of gray shades available for the computer to “choose from”
-All the possible shades
-Represented in powers of 2:
2^4= has 16 shades
2^5= has 64 shades
2^16= has 4096 shades

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10
Q

Step 3 Quantization: Dynamic Range

A

All the chosen shades of gray

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11
Q

Step 3 Quantization: Grayscale

A

All the visible shades of gray

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12
Q

Step 4: Coding

A

The computer assigns binary code that represents that grayshade in that pixel by its location on the matrix

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13
Q

What is the purpose of digital image processing?

A

-Prepare: Correct for inherent inconsistencies and shift grayscale into human vision
-Optimize: Enhance/suppress anatomic details
-Analyze: Computer Aided Diagnosis (CAD)

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14
Q

Pre-Processing

A

corrections made to the raw digital image that are designed to normalize the image, preparing

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15
Q

Post-processing

A

refinements to digital image that are targeted towards the specific anatomy, to make it look how we want it to

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16
Q

Pre-processing: acquisition processing

A

corrections made to raw image data to correct for

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17
Q

What does pre-processing correct for?

A
  • Inconsistencies in the x-ray beam
  • Electronic noise
  • Non-uniformity in screen thickness (CR)
  • Non-uniformity in Laser scanning (CR)
  • Accumulated Background Exposure
  • Faulty Pixels or Detector elements (DEL)
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18
Q

Post-Processing

A

• All manipulation of the digital image data made after corrections have been made for data acquisition
• Default processing
• Operator functions

19
Q

Domain Processing

A

How the computer sorts the data for efficient processing

20
Q

What are the 3 processing domains

A

-Spatial domain
-Intensity domain
-Frequency domain

21
Q

Spatial Domain

A

Sorts the information by location on the Detector

•Sorts image data by location of pixels in space
•Radiographs start and end in the spatial domain
•Spatial Processing tasks:
• Point processing
• Area/Local processing
• Global processing

22
Q

Intensity Domain

A

Sorts the information by pixel value (gray shade)

• Histogram construction and analysis
• Application of the LUT

23
Q

Frequency Domain

A

Sorts the information by size of structure, according to the frequency of signal
• Small structures generate high frequency
• Large structures generate low frequency

*high pass filter
*low pass filter

24
Q

3 types of processing algorithms that occur in the spatial domain

A

-Point processing
-Area/Local processing
-Global processing

25
Point processing
Perform tasks on individual pixels
26
Area/Local processing
-Kernel: A matrix within a matrix -Convolution Kernels: Applies the same weighted calculation to each pixel in the kernel -Interpolation Kernels: Takes the average of the pixels and applies that value to the center pixel
27
Global processing
Massive spatial function across entire image
28
What is a Kernel? What are 2 common types of kernels?
-A matrix within a matrix -Interpolation kernel and convolution kernels
29
Convolution Kernels
Applies the same weighted calculation to each pixel in the kernel
30
Interpolation Kernels
Takes the average of the pixels and applies that value to the center pixel
31
Histogram
• A graphical representation of the pixel values in an image (gray shades within an image) • Histogram selected by radiographer
32
In what processing domain is the histogram constructed and analyzed?
Intensity domain
33
Fourier Transform
Algorithm that transforms image data from spatial domain to frequency domain
34
What processing domain is the Fourier Transform used in?
Frequency domain
35
Reverse Fourier Transform
Restacks the individual frequencies within the image data into a single frequency pattern
36
2 types of Frequency domain processing algorithms
-High pass filter -Low pass filter
37
High pass filter
High frequencies "pass" onto the image and low frequencies are suppressed • Edge Enhancement Filters
38
Low pass filter
Low frequencies "pass" onto the image and high frequencies are suppressed • Smoothing Filters
39
Processing Algorithms
*computer may perform these tasks in multiple domains -Image to Image transformations: Grayscale shifting -Image to information transformations: Fourier transform -Information to image transformations: used to display processed data
40
The smaller the pixel pitch the _________ the resolution
greater
41
Sample
representative part of a population, used for determining the characteristics of the whole population
42
sampling frequency or pitch
The number of samples taken per unit of length
43
sampling frequency is measured in
pixels/mm
44
Sampling Pitch
space between samples