Image data manipulation Flashcards
Why is digital image processing useful?
Allows the acquired image to be manipulated to improve the display
What is a digital image?
A function with x and y co-ordinates and an intensity function (greyscale function), each with finite discrete quantities
What are the three approaches to image processing?
Point operations
Local operations
Global operations
What are point operations?
Calculate a new pixel value for each pixel in an image
What are the five common point operations?
Inversion Contrast enhancement/stretching Thresholding Windowing DICOM display calibration curve
What is windowing and leveling?
Applies a look up table to adjust the relationship between pixel values and displayed values
What are local operations?
The application of a kernel to the image via a convolution in order to apply a filter or window a portion of the image
What are global operations?
Processes that manipulate the image as a whole with a calculation performed in frequency space rather than real space
Why are global operations performed in frequency space?
The same processes are performed quickly with fewer steps and calculations
How do global operations work?
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
What image registration and what is its purpose?
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
What is the definition of image registration?
The mathematical operation of aligning two or more image datasets so that similar or complementary information can be transformed onto a common reference
What is the definition of image fusion?
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
What are the four things that are needed for an image registration algorithm?
A metric
A transform
An optimiser
An interpolator
What is the metric?
A measure of how similar the two images are, often the mean square difference. This is what is optimised
What is the transform?
A mathematical method for moving the one image to match another, ie a transformation
What is the process of optimisation?
Minimise the MSD using transformations - calculate it for all possible transforms
Select the transform with the lowest MSD
What are the three types of transformations and how many degrees of freedom do they allow?
Rigid - translations 3DoF, Rotations 6DoF
Affine - Scaling/sheering 12DoF
Deformable - n DoF
How is interpolation used in image registration?
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
What are the three common interpolation methods?
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
Why is the B-spline value of an interpolation method important?
Higher B-spline = more accurate interpolation but more computing power required
What are the five most common kinds of metric used?
Mean squared difference Sum of squared difference Correlation coefficient Ratio image uniformity Mutual information
When are MSD and SSD metrics used?
Geometric only problems
Pixel intensity based problems only
Only mono-modality as very sensitive to a few pixels with large intensity differences
How does mutual information work?
Uses entropy and produces image histograms for the separate and combined images. Therefore the on image probability predicts the other image
What is the equation for the entropy of a message?
H = nlog(s) where n = length of message and s = number of symbols
As the number of messages increases what happens to the amount of information gained and the uncertainty of it being each message?
Information and uncertainty increases
How did Shannon entropy include the probability of a message?
H = sum((1/s^n)log(s^n)) - a weighted measure of entropy
The rarer an event, the more information contained
How is Shannon entropy used for images?
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
What is mutual information used for?
Registration of multimodality images. Assumes regions of similar grey levels in one image correspond to the same region in another
How are joint histograms constructed?
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
How are joint histograms used for optimisation?
Histogram shows increasing dispersion as the mis-registration increases
Can estimate joint probability distribution using H = -sum(p(i,j).log(p(i,j)))
How can difference images be used in opitimsation?
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
What is the mathematical equation for mutual information?
I(A,B) = H(A) + H(B) - H(A,B)
What is the advantage in using mutual information over joint entropy?
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
How is image registration accuracy assessed?
In commissioning - through the use of gold standards
In clinic - visually
