Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

Matt’s Class > Ch. 31 > Flashcards

Ch. 31 Flashcards

1
Q

What are the 3 general approaches to processing any digital image?

A

Spatial, Intensity, and Frequency Domain

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Point, area, and global processing, and the application of kernels, are all operations in what domain?

A

Spatial Domain

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

In which of the general digital processing domains is the image sorted out according to pixel values?

A

Intensity Domain

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Which of the general digital processing domains relates to the objects in the image rather than the pixels?

A

Frequency Domain

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

The zoom or magnification feature is an example of (point, area, or global) processing?

A

Area Processing

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Every image begins and end with ? domain.

A

Spatial

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

As pixels are sorted into other domains, the computer keeps track of their ? locations, so they can be placed black.

A

Matrix

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Sorting an image by the intensities of its pixels results what?

A

A histogram

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Sorting an image by object ? results in a frequency distribution.

A

Size

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

The histogram plots the number of ? against each pixel value.

A

Pixels

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

The frequency distribution plots the number of objects or details against their ?

A

Size

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

While the image is separated into the intensity domain (histogram), specific ? can be targeted to be altered.

A

Densities

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

While the image is separated into the frequency domain, specific sizes of objects or details can be targeted for ? or ?

A

Suppression or Enhancement

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

When intensity or frequency operations are complete, the changed data are placed back into the pixels of the ? matrix to reconstruct the image.

A

Spatial

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

For gradation processing, the rescaled data set is fed into an anatomical ? that was determined when the operator selected the procedure at the console.

A

LUT

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

When a gradient curve is plotted on a graph showing the various densities in an image, the average brightness of the image is represented by the left to right ? of this curve, and the contrast as how steep the curve is.

A

Position

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

A look up table really is a simple table with two columns: One for ? and one for ?

A

Input or Output

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

The conversion of input values to out values is generated by mathematical ? that can be represented as a ? curve on a graph.

A

Formula; Function

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

The types of formulas used for gradation processing are referred to as ? transformations.

A

Intensity

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

If the dynamic range or bit depth of a digital processing system is too limited, it is possible for data ? to occur when either brightness or contrast adjustments are made.

A

Clipping

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

Bit depth is the range of different gray levels for a computer, LCD monitor or other ? devices.

A

Hardware

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

? Is the range of different gray levels made available by a computer system including it’s installed software.

A

Dynamic range

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

Gray scale is the range of different gray (brightness) levels present in a ? image.

A

Displayed

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

With conventional radiography,when soft tissue techniques were used to demonstrate soft tissue areas, the ? were then depicted too light.

A

Bones

25
Q

In dynamic range compression, compressing the gray scale curve brings extremely light densities in the image up to a ? level, and at the same time extremely dark areas are made ?

A

Darker; Lighter

26
Q

Since the actual gray scale of the image does not use up to the entire dynamic range in the system, the ? image can still be darkened or lightened as a whole

A

Displayed

27
Q

Since the bit depth of a computer can far exceed the range of human vision memory ? can still be darkened or lightened as a whole.

A

Storage space

28
Q

Mathematically, DRC finds the mid-point of the gray scale curve, (the average brightness or density level), then progressively ? pixel values above this point, and progressively ? pixel values below it.m

A

Reduces; Increases

29
Q

Applied to a degree visibly affecting the image, DRC results in tissue ? which takes out the darkest and lightest densities in the image.

A

Equalization

30
Q

Tissue equalization or contrast equalization can simulate the traditional ? technique.

A

Soft tissue

31
Q

Tissue equalization is actually gray scale truncation - Elimination of darkest and lightest densities which results in a ? looking image.

A

Grayer

32
Q

Detail processing is characterized by its ability to treat fine details as a ? component of the image, without changing the overall brightness or contrast.

A

Separate

33
Q

Detail processing can be performed either in the ? domain or in the ? domain.

A

Frequency; Spatial

34
Q

For the first step in unsharp mask filtering, a mask image is created which contains only the ? structures in the image.

A

Gross

35
Q

The “unsharp” mask is not really geometrically blurred; it appears blurry or unsharp because the ? details have been removed through averaging.

A

Finer

36
Q

Structures that are smaller than the kernel size are ? and no longer visible in this mask image.

A

Suppressed

37
Q

The larger the size of the ? matrix used, the wider the region used for averaging, and the more “blurred” the mask image appears.

A

Kernel

38
Q

In the second step for unsharp mask filtering a ? of the “unsharp” mask is created by image reversal.

A

Positive

39
Q

In step 3 for unsharp mask filtering, the positive mask is effectively superimposed over the original image, such that positive and negative pixel values cancel each other out for anything present on ? images.

A

Both

40
Q

The net result is ? (High-pass filtering)

A

Edge enhancement

41
Q

Edge enhancement features such as blurred mask subtraction should not be over-used, because they also enhance the levels of ? in the image.

A

Noise

42
Q

The application of too small a kernel may remove ? from the image such that diagnostic information is lost.

A

Details

43
Q

Just the opposite of unsharp mask filtering, kernels can also be used to suppress image ?

A

Noise

44
Q

? is a form of low-pass filtering that softens edges and reduces noise.

A

Smoothing

45
Q

Excessive noise reduction can lead to ? of details.

A

Loss

46
Q

In an image that already has ? contrast, applying smoothing can lead to loss of detail.

A

Low

47
Q

Kernels work better for suppressing ? noise, such as quantum mottle, whereas frequency processing works better for periodic noise.

A

Random

48
Q

An image can be represented not only as a collection of pixels in space, but also as a collection of waves with different ?

A

Frequencies

49
Q

3 steps for final preparation of image display:

A
  1. Noise reduction
  2. Additional gradation processing
  3. Formatting for display
50
Q

Because edge enhancement, used universally for detail processing, results in increased noise in the image, noise reduction must ? be applied at this stage.

A

Again

51
Q

Two general types of noise are ? noise and ? noise.

A

Random; Periodic

52
Q

Periodic noise consists of artifacts that tend to be of roughly ? size and occur in a regular ?. An example is electronic “snow”

A

Consistent; Pattern

53
Q

By reducing noise in any form, all-important ? is enhanced.

A

Signal - to - noise ratio

54
Q

Frequency processing is ideal for removing electronic an other periodic forms of noise, because these artifacts all occur in the ? image detail layer, while normal anatomy is spread across several image layers.

A

Same

55
Q

Kernels work better for suppressing ? noise, such as quantum mottle, that occur in several image detail layers.

A

Random

56
Q

? Is defined as the difference in signal contribution (intensity) between two different image densities or specific tissue areas, divided by the background noise level.

A

Contrast - to - noise ratio (CNR)

57
Q

Formula for CNR:

A

CNR = T1-T2/N

58
Q

Noise levels can be measured with test objects presenting ? between lines or spots.

A

Just Noticeable Differences

59
Q

Some manufacturers “calculate” a “CNR image” for subtraction from the overall image to reduce noise. In reality, the size of the ? or noise artifacts is determined, and the frequency layer is deleted.

A

Granularity

Matt’s Class (2 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions