Brainscape's Knowledge GenomeTM

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


See full index

Year 3: Imaging > Image data manipulation > Flashcards

Image data manipulation Flashcards

1
Q

Why is digital image processing useful?

A

Allows the acquired image to be manipulated to improve the display

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

What is a digital image?

A

A function with x and y co-ordinates and an intensity function (greyscale function), each with finite discrete quantities

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

What are the three approaches to image processing?

A

Point operations
Local operations
Global operations

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

What are point operations?

A

Calculate a new pixel value for each pixel in an image

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

What are the five common point operations?

A 
Inversion
Contrast enhancement/stretching
Thresholding
Windowing
DICOM display calibration curve
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

What is windowing and leveling?

A

Applies a look up table to adjust the relationship between pixel values and displayed values

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

What are local operations?

A

The application of a kernel to the image via a convolution in order to apply a filter or window a portion of the image

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

What are global operations?

A

Processes that manipulate the image as a whole with a calculation performed in frequency space rather than real space

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

Why are global operations performed in frequency space?

A

The same processes are performed quickly with fewer steps and calculations

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

How do global operations work?

A

Apply a fourier transform to both the image and the kernel
Convolve in fourier space by multiplying the transformed image and kernel
Apply the inverse transform

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

What image registration and what is its purpose?

A

The process of finding the spatial transforms that maps points on an object in one image to points on an object in another image. Usually used for aligning medical images

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

What is the definition of image registration?

A

The mathematical operation of aligning two or more image datasets so that similar or complementary information can be transformed onto a common reference

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

What is the definition of image fusion?

A

The process of combining information in two or more image datasets into a more informative display. Accurate image registration is a pre-requisite of image fusion

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

What are the four things that are needed for an image registration algorithm?

A

A metric
A transform
An optimiser
An interpolator

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

What is the metric?

A

A measure of how similar the two images are, often the mean square difference. This is what is optimised

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

What is the transform?

A

A mathematical method for moving the one image to match another, ie a transformation

17
Q

What is the process of optimisation?

A

Minimise the MSD using transformations - calculate it for all possible transforms
Select the transform with the lowest MSD

18
Q

What are the three types of transformations and how many degrees of freedom do they allow?

A

Rigid - translations 3DoF, Rotations 6DoF
Affine - Scaling/sheering 12DoF
Deformable - n DoF

19
Q

How is interpolation used in image registration?

A

If the spatial moves do not correspond to whole numbers of pixels can interpolate between overlapping pixels in spatial coordinates
Must be carried out during optimisation

20
Q

What are the three common interpolation methods?

A

Nearest neighbour - intensity of voxel nearest in distance is returned
Linear - returned value is a weighted average of the surrounding voxels, with the distance to each voxel taken into account
Polynomial

21
Q

Why is the B-spline value of an interpolation method important?

A

Higher B-spline = more accurate interpolation but more computing power required

22
Q

What are the five most common kinds of metric used?

A 
Mean squared difference
Sum of squared difference
Correlation coefficient
Ratio image uniformity
Mutual information
23
Q

When are MSD and SSD metrics used?

A

Geometric only problems
Pixel intensity based problems only
Only mono-modality as very sensitive to a few pixels with large intensity differences

24
Q

How does mutual information work?

A

Uses entropy and produces image histograms for the separate and combined images. Therefore the on image probability predicts the other image

25
Q

What is the equation for the entropy of a message?

A

H = nlog(s) where n = length of message and s = number of symbols

26
Q

As the number of messages increases what happens to the amount of information gained and the uncertainty of it being each message?

A

Information and uncertainty increases

27
Q

How did Shannon entropy include the probability of a message?

A

H = sum((1/s^n)log(s^n)) - a weighted measure of entropy

The rarer an event, the more information contained

28
Q

How is Shannon entropy used for images?

A

It is a probability distribution of the occurrence of grey values
low entropy - zero info
High entropy - equal quantities of lots of grey values - lots of info

29
Q

What is mutual information used for?

A

Registration of multimodality images. Assumes regions of similar grey levels in one image correspond to the same region in another

30
Q

How are joint histograms constructed?

A

Intensities/probabilities from one image are plotted against the second image to create feature space
Feature space is constructed by counting the number of times a combination of grey values occur
x axis is from image 1, y from image 2
Each time the particular combination of grey values are found the entry in feature space increases

31
Q

How are joint histograms used for optimisation?

A

Histogram shows increasing dispersion as the mis-registration increases
Can estimate joint probability distribution using H = -sum(p(i,j).log(p(i,j)))

32
Q

How can difference images be used in opitimsation?

A

Subtract the two images - perfectly aligned gives a uniform intensity, slightly misaligned gives edge features
Therefore there is an increased entropy, so register by iteratively attempting to minimise the entropy of the difference image

33
Q

What is the mathematical equation for mutual information?

A

I(A,B) = H(A) + H(B) - H(A,B)

34
Q

What is the advantage in using mutual information over joint entropy?

A

It includes the entropies from the separate images so it will not produce low values for complete misregistrations where only the backgrounds overlap - it is also less sensitive to the size of the overlap

35
Q

How is image registration accuracy assessed?

A

In commissioning - through the use of gold standards

In clinic - visually

Year 3: Imaging (6 decks)

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