Finals | Pre/Post-medical Image Processing

Question 1

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

Answer 1

2-D image reconstruction.

Question 2

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

Answer 2

Image reconstruction theorem

Question 3

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

Answer 3

Fourier transform (FT)

Question 4

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.

Answer 4

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

Question 5

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

Answer 5

1. background
2. Background removal

Question 6

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

Answer 6

A. Sharpness
B. Resolving Power
C. Noise Level

S RP Noise

Question 7

Inherited from the design of the instrumentation

Answer 7

A. Sharpness
B. Resolving Power

Question 8

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

Answer 8

C. Noise Level

Question 9

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

Answer 9

1. photon fluctuations
2. electronic noise

Question 10

Means that the image is less noisy.

Answer 10

High signal-to-noise ratio (SNR)

Question 11

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

Answer 11

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

Question 12

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.

Answer 12

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

Question 13

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

Answer 13

Image processing

Question 14

Noise Reduction Filters:

Answer 14

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

GAM AD BiNon

Question 15

Simplest form of filtering

Answer 15

Block Filtering/ Averaging

Question 16

Blurs the image and are classified by their sigma or width

Answer 16

Gaussian filter

Question 17

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

Answer 17

Median Filtering

Question 18

Smoothens regions without strrong edges while preserving edges

Answer 18

Anistropic diffusion filtering

Question 19

Recently developed method similar to the Gaussian filter

Answer 19

Bilateral filtering

Question 20

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

Answer 20

Nonlocal Means Filter

Question 21

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

Answer 21

IMAGE COMPRESSION

Question 22

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

Answer 22

1. images
2. disk or memory space

Question 23

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

Answer 23

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

Question 24

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

Answer 24

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

Question 25

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

Answer 25

1. Lossless 2. Lossy

Question 26

Achieves higher compression levels

Answer 26

lossy compression algorithms

Question 27

Pre-medical image process

Answer 27

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

Question 28

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

Answer 28

I. FILTERING

Question 29

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

Answer 29

1. removed 2. enhanced 3. calculation

Question 30

Subtopics under filtering

Answer 30

A. HISTOGRAM MANIPULATION B. ENHANCEMENT

Question 31

Standard window/level manipulation

Answer 31

HISTOGRAM MANIPULATION

Question 32

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

Answer 32

HISTOGRAM MANIPULATION

Question 33

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

Answer 33

HISTOGRAM MANIPULATION

Question 34

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

Answer 34

1. contrast 2. studies

Question 35

Window width vs window level

Answer 35

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

Question 36

Two commonly used enhancement in medical imaging are

Answer 36

1. Unsharp masking 2. Edge sharpening filters

Question 37

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

Answer 37

Unsharp masking

Question 38

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).

Answer 38

1. Unsharp masking 2. masked 3. unmodified

Question 39

Selectively enhance edges in the image based on edge strength.

Answer 39

Edge sharpening filters

Question 40

Process of becoming smaller

Answer 40

Contraction

Question 41

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

Answer 41

Enhancement

Question 42

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.

Answer 42

1. enhancement 2. gray levels 3. pseudo colors

Question 43

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.

Answer 43

REGISTRATION

Question 44

Registration may be

Answer 44

1. Intra-subject 2. Inter-subject 3. Intra-modality 4. Inter-modality

Question 45

Intra-subject

Answer 45

within the same subject

Question 46

Inter-subject

Answer 46

across different subjects

Question 47

Intra-modality

Answer 47

using images from one modality

Question 48

Inter-modality

Answer 48

using images from multiple modalities

Question 49

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.

Answer 49

1. classification

Question 50

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.

Answer 50

1. likelihood of malignancy 2. differential diagnoses

Question 51

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

Answer 51

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

Question 52

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

Answer 52

a. statistical b. transform c. model-based

Question 53

based on the description of image texture by unique statistical features

Answer 53

statistical

Question 54

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

Answer 54

transform

Question 55

mathematical model is developed that represents the texture.

Answer 55

model-based

Question 56

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

Answer 56

Image segmentation

Question 57

It divides an image into areas based on a specified description

Answer 57

Image segmentation

Question 58

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

Answer 58

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

Question 59

Post-medical Image Process

Answer 59

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