Spatial Image Enhancement

Question 1

Image enhancement

Answer 1

• Transformations that highlight image features
• mathematical manipulations of data
• new data created from raw data

Question 2

Fourier methods

Answer 2

• Performed in the frequency

Question 3

9-point smoothing filter

Answer 3

• applied to all central pixels, not pixels around edges
• convolution kernal (mask) makes pixels more alike

Question 4

Weighted smoothing filter

Answer 4

• convolution kernel is weighted
• pixel weight determined by proximity to central pixel
• less blurring and better spatial resolution compared to unweighted smoothing
• AKA replaces the averaging
• new pixel value= Σ(wt. x pixel value)/16(total wt. of kernal)

Question 5

Edge enhancement filter

Answer 5

• opposite to image smoothing
• uses the same pixel replace averaging technique
• negative coefficients used in weighting
• kernel enhances edges, increasing contrast.

Question 6

Mask Size

Answer 6

• The mask or kernel always has an odd number of rows and columns of pixels (edge enhancement and smoothing)
• Dimensions vary according to the equation (2n+1) x (2n+1)
• 3x3, 5x5, 7x7

Question 7

Point processing operations

Answer 7

• Manipulations of the number of cts/pixel
• completely independant of neighbouring pixels
• background subtraction, gray scales, and color tables

Question 8

Background subtraction

Answer 8

• Neighbourhood independant
• subtracting pre a determined, constant number of counts from each pixel in an image
• Improves contrast, but comes at the cost of increased noise

Question 9

Interpolated background subtraction

Answer 9

• neighbourhood dependant
• does not assume that background is uniform, but varies with position in the image
• creates an ROI, all pixels outside ROI are considered background
• background pixels are weighed according to their distance from ROI

Question 10

Gray scale

Answer 10

• neighbourhood independant, referred to also as the dynamic range
• number of shades between complete black and complete white
• translating the number of counts in a pixel to an integer, which in turn defines the pixels color

Question 11

Linear Gray Scale

Answer 11

• Gray scale assigned is proportional to the number of counts in the pixel
• The pixel with the highest number of counts is assigned white and the pixel with no counts is assigned black
• all other pixels are given a gray scale proportional to the ratio of counts in each pixel to the maximum counts per pixel
• Normalizes pixel counts

Question 12

Non-normalized gray scale

Answer 12

• Better suited for low count images
• causes loss of image contrast in high count images
• Limits dynamic range
• If hottest pixel is 200 counts, the gray level is 200
• all pixels are given the shade that corresponds to the absolute count/pixel value

Question 13

Thresholding gray scale

Answer 13

• a variation of linear gray scale
• eliminates background
• cuts off lower percentage of maximum counts
• all pixels less than a set percentage of maximum counts will appear black and the gray scale will increase from there

Question 14

Logarithmic and exponential gray scale

Answer 14

• grey scales can be logarithmic or exponential in design
• logarithmic enhance image contrast in low count areas and decreases it in high count areas
• exponential is opposide to logarithmic
• both should be used cautiously as they can result in false interpretation of RP distribution

Question 15

color translation tables

Answer 15

• lookup tables used to assign colors or different intensities of the same color to each pixel as a function of pixel counts
• thre intensity scales are used: red, green, blue
• user preference

Question 16

Quantitative analysis

Answer 16

• ROIs
• curve generation and analysis

Question 17

Types of ROI

Answer 17

• manual
• semi-automated
• automated

Question 18

manual ROIs

Answer 18

visual determination with manual use of mouse to draw ROI

Question 19

Semi- automated ROIs

Answer 19

Tech has some input to alter the ROI

Question 20

Automated ROIs

Answer 20

• Tech shows computer general area
• computer uses edge detection to determine ROI

Question 21

Time activity curves

Answer 21

Determine flow characterisitices of an RP in the body

Question 22

Issues with TACs

Answer 22

• Noise interference
• poor counts
• inter-technologist differences in ROI drawing

Question 23

Smoothing methods for curves

Answer 23

• manual smoothing
• moving average
• weighted moving average

Question 24

Manual smoothing

Answer 24

• simple
• time consuming
• human bias

Question 25

Moving average

Answer 25

• three point smooth
• ynew = y(n-1) + y(n) + y(n+1)/3
• five point smooth

Question 26

weighted moving average

Answer 26

• as data moves further from principle point, they are given less weight
• Ynew = 1/∑wi x ∑wiYi

Question 27

Regression analysis

Answer 27

• a more sophisticated version of smoothing
• analytic smoothing
• assumes curve should follow a mathematical function
• derives new curve according to the desired mathematical function with the least amount of deviation from the original data
• aka least squares curve fitting

Question 28

Smoothing methods for wall motion studies

Answer 28

• spatial smoothing
• temporal smoothing
• count normalization
• count poor wall motion images are enhanced before the cine is run
• may go through more than one post-processing program