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1006 RAD TECH 131 > Finals | Pre/Post-medical Image Processing > Flashcards

Finals | Pre/Post-medical Image Processing Flashcards

1
Q

Let f (x, y) be a two-dimensional (2-D) cross- sectional image of a three-dimensional object.

A

2-D image reconstruction.

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2
Q

States that f (x, y) can be reconstructed from the cross-sectional one-dimensional projections.

A

Image reconstruction theorem

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3
Q

Mathematically, the image reconstruction theorem can be described with the help of —

A

Fourier transform (FT)

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4
Q

II. BACKGROUND REMOVAL Backgrounds can be removed in both (1) and (2) images. The idea is to (3) by discarding the (4) from the image.

A
  1. sectional (cross-section)
  2. projection
  3. reduce the size of the image file
  4. background
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5
Q

II. BACKGROUND REMOVAL
In digital images, unexposed areas appearing white on a display monitor is defined as (1) in this context.
(2) in this context means that the brightness of the background is converted from white to black

A
  1. background
  2. Background removal
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6
Q

III. NOISE REMOVAL
Quality of an image can be measured from:

A

A. Sharpness
B. Resolving Power
C. Noise Level

S RP Noise

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7
Q

Inherited from the design of the instrumentation

A

A. Sharpness
B. Resolving Power

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8
Q

Arises from photon fluctuations from the energy source and detector system used, and electronic noise accumulated through the imaging chain.

A

C. Noise Level

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9
Q

Noise Level arises from (1) from the energy source and detector system used, and (2) accumulated through the imaging chain.

A
  1. photon fluctuations
  2. electronic noise
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10
Q

Means that the image is less noisy.

A

High signal-to-noise ratio (SNR)

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11
Q

A common method to increase the SNR (i.e., reduce the noise in the image) is to —

A

obtain many images of the same object under the same conditions and average them

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12
Q

III. NOISE REMOVAL
Noise is present in all medical images. We can reduce the amount of noise by increasing (1) or increasing (2). (3) also offers ways to reduce noise, but sometimes at the cost of introducing artifacts.

A
  1. radiation dose (for X-ray methods)
  2. field strength or imaging time (MR)
  3. Image processing
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13
Q

Also offers ways to reduce noise, but sometimes at the cost of introducing artifacts.

A

Image processing

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14
Q

Noise Reduction Filters:

A
  1. Block Filtering/ Averaging
  2. Gaussian filter
  3. Median filtering
  4. Anisotropic diffusion filtering
  5. Bilateral filtering
  6. Nonlocal means filter

GAM AD BiNon

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15
Q

Simplest form of filtering

A

Block Filtering/ Averaging

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16
Q

Blurs the image and are classified by their sigma or width

A

Gaussian filter

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17
Q

Considers a patch of neighborhood pixels. They exhibit less blurring than averaging and gaussian filters

A

Median Filtering

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18
Q

Smoothens regions without strrong edges while preserving edges

A

Anistropic diffusion filtering

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19
Q

Recently developed method similar to the Gaussian filter

A

Bilateral filtering

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20
Q

Newer technique that examines a “patch” centered about each pixel.
Main concept: similar patches, when averaged, will wash out noise

A

Nonlocal Means Filter

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21
Q

Process applied to a graphics file to minimize its size without degrading image quality below an acceptable threshold.

A

IMAGE COMPRESSION

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22
Q

IV. IMAGE COMPRESSION
By reducing the file size, more (1) can be stored in a given amount of (2).

A
  1. images
  2. disk or memory space
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23
Q

IV. IMAGE COMPRESSION
Image compression attempts to (1) to minimize the (2) for a given image.

A
  1. reduce or eliminate the presence of redundancies
  2. storage size or transmission time requirements
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24
Q

IV. IMAGE COMPRESSION
In general, three types of redundancies can be identified that are relevant to compressed medical images:

A

a. Coding (R/IR)
b. Spatial, temporal, and bit depth (R/IR)
c. Psychovisual (IR)

25
Q

IV. IMAGE COMPRESSION
Reversible (R): (1)
Irreversible (IR): (2)

A
  1. Lossless
  2. Lossy
26
Q

Achieves higher compression levels

A

lossy compression algorithms

27
Q

Pre-medical image process

A

I. IMAGE RECONSTRUCTION
II. BACKGROUND REMOVAL
III. NOISE REMOVAL
IV. IMAGE COMPRESSION

28
Q

A processing method that enhances or removes a specific component in a signal or image.

A

I. FILTERING

29
Q

I. FILTERING
The name could reflect what is (1), what is (2), or the (3) that is used.

A
  1. removed
  2. enhanced
  3. calculation
30
Q

Subtopics under filtering

A

A. HISTOGRAM MANIPULATION
B. ENHANCEMENT

31
Q

Standard window/level manipulation

A

HISTOGRAM MANIPULATION

32
Q

Reducing the window width increases contrast, while wider windows reduce contrast.

A

HISTOGRAM MANIPULATION

33
Q

This technique is useful for visual comparison across patients in MR exams.

A

HISTOGRAM MANIPULATION

34
Q

Histograms may also be matched between images to produce similar (1) between images from different (2).

A
  1. contrast
  2. studies
35
Q

Window width vs window level

A

Window width: Adjusting the contrast (x-axis)
Window level: Adjusting the density (y-axis)

36
Q

Two commonly used enhancement in medical imaging are

A
  1. Unsharp masking
  2. Edge sharpening filters
37
Q

A standard enhancement filter that selectively subtracts a blurred image from the original.

A

Unsharp masking

38
Q
  1. (1): Pixels in the blurred image that differ from the original by an amount more than a user-specified threshold are considered to be ‘‘(2).’’ Any pixel under the mask is subtracted from the original, otherwise the pixel is (3).
A
  1. Unsharp masking
  2. masked
  3. unmodified
39
Q

Selectively enhance edges in the image based on edge strength.

A

Edge sharpening filters

40
Q

Process of becoming smaller

A

Contraction

41
Q

Refers to any method that makes particular elements in the image more visible (in the context of image post-processing)

A

Enhancement

42
Q

Color, when used, is mostly for (1), namely by a range of (2) converted to (3) in order to enhance the visual appearance of features within this range.

A
  1. enhancement
  2. gray levels
  3. pseudo colors
43
Q

Process of aligning images with one another. This means that the same tissue sample exists at a given X, Y, Z location on all registered images.

A

REGISTRATION

44
Q

Registration may be

A
  1. Intra-subject
  2. Inter-subject
  3. Intra-modality
  4. Inter-modality
45
Q

Intra-subject

A

within the same subject

46
Q

Inter-subject

A

across different subjects

47
Q

Intra-modality

A

using images from one modality

48
Q

Inter-modality

A

using images from multiple modalities

49
Q

After relevant image features are extracted, (1) ensues to derive a usable output (e.g., likelihood of malignancy, differential diagnoses) from the analysis of the features.

A
  1. classification
50
Q

A. CLASSIFICATION
After relevant image features are extracted, classification ensues to derive a usable output (e.g., (1), (2)) from the analysis of the features.

A
  1. likelihood of malignancy
  2. differential diagnoses
51
Q

A. CLASSIFICATION
Depending on the clinical context for the particular patient who underwent the imaging study, the final output may include

A
  1. detection of an abnormality
  2. description/characterization of a known abnormality
  3. providing a diagnosis or list of differential diagnoses
  4. evaluation of disease progression
52
Q

B. TEXTURING
* Texture analysis methods can be categorized into three main approaches:

A

a. statistical
b. transform
c. model-based

53
Q

based on the description of image texture by unique statistical features

A

statistical

54
Q

based on the transformation of the image using for example Fourier or wavelet approaches and characterizing texture in the transform domain

A

transform

55
Q

mathematical model is developed that represents the texture.

A

model-based

56
Q

Most essential medical imaging process as it extracts the region of interest (ROI) through a semiautomatic or automatic process.

A

Image segmentation

57
Q

It divides an image into areas based on a specified description

A

Image segmentation

58
Q

C. SEGMENTATION
It divides an image into areas based on a specified description, such as

A
  1. segmenting body organs/tissues in the medical applications for border detection
  2. tumor detection/ segmentation
  3. mass detection.
59
Q

Post-medical Image Process

A

I. FILTERING
A. HISTOGRAM MANIPULATION:
B. ENHANCEMENT:
II. CONTRACTION AND ENHANCEMENTS
III. REGISTRATION
IV. CLASSIFICATION, TEXTURING, AND SEGMENTATION

1006 RAD TECH 131 (16 decks)

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