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SPRING 2024: Principles II > UNIT 2: Image Processing Basics > Flashcards

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
Q

Point processing

A

Perform tasks on individual pixels

26
Q

Area/Local processing

A

-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
Q

Global processing

A

Massive spatial function across entire image

28
Q

What is a Kernel? What are 2 common types of kernels?

A

-A matrix within a matrix
-Interpolation kernel and convolution kernels

29
Q

Convolution Kernels

A

Applies the same weighted calculation to each pixel in the kernel

30
Q

Interpolation Kernels

A

Takes the average of the pixels and applies that value to the center pixel

31
Q

Histogram

A

• A graphical representation of the pixel values in an image (gray shades within an image)
• Histogram selected by radiographer

32
Q

In what processing domain is the histogram constructed and analyzed?

A

Intensity domain

33
Q

Fourier Transform

A

Algorithm that transforms image data from spatial domain to frequency domain

34
Q

What processing domain is the Fourier Transform used in?

A

Frequency domain

35
Q

Reverse Fourier Transform

A

Restacks the individual frequencies within the image data into a single frequency pattern

36
Q

2 types of Frequency domain processing algorithms

A

-High pass filter
-Low pass filter

37
Q

High pass filter

A

High frequencies “pass” onto the image and low frequencies are suppressed
• Edge Enhancement Filters

38
Q

Low pass filter

A

Low frequencies “pass” onto the image and high frequencies are suppressed
• Smoothing Filters

39
Q

Processing Algorithms

A

*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
Q

The smaller the pixel pitch the _________ the resolution

A

greater

41
Q

Sample

A

representative part of a population, used for determining the characteristics of the whole population

42
Q

sampling frequency or pitch

A

The number of samples taken per unit of length

43
Q

sampling frequency is measured in

A

pixels/mm

44
Q

Sampling Pitch

A

space between samples

SPRING 2024: Principles II (13 decks)

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