mutual information as a measure for image quality of temporally subtracted chest radiographs...
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![Page 1: Mutual Information as a Measure for Image Quality of Temporally Subtracted Chest Radiographs Samantha Passen Samuel G. Armato III, Ph.D](https://reader030.vdocuments.site/reader030/viewer/2022032800/56649d4c5503460f94a2a814/html5/thumbnails/1.jpg)
Mutual Information as a Measure for Image
Quality of Temporally Subtracted Chest
Radiographs
Samantha PassenSamuel G. Armato III, Ph.D.
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Introduction Commonly, radiologists compare multiple
chest radiographs side-by-side
Current Previous
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Introduction Kano et al. (1994)- Temporal Subtraction
Detect ribcage edges and denote “lung mask”
Current
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Introduction ROIs involved in nonlinear geometric warping
to align previous image to current
Current Warped Previous
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Introduction Temporal Subtraction
Image
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Related Work Difazio et. Al (1997) demonstrated improved
radiologist diagnostic accuracy with temporal subtraction images
Ishida et. Al. (1999) used local cross-correlation method to maximize alignment
Armato et al. (2006) – automated identification of registration accuracy Feature-based linear discriminant analysis Based on radiologist ratings of images
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Motivation
While temporal subtraction images effectively enhance areas of pathologic change, misregistration of the images can mislead radiologists in diagnosis by obscuring or creating interval change
Mutual information as a metric to quantify misregistered cases
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Related Work
Mutual Information confirmed to:
Coselmon et al. (2004)- register volumetric image data
Sanjay-Gopal et al. (1999) – register mammograms
Pluim et al. (2003) – transformation technique to align images
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Mutual Information (MI) Joint Histogram:
Lower Entropy------------------Higher Entropy
(misregistration) Mutual Information:
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Materials Radiologists rated 138 temporal
subtraction images from 1.0-5.9
Rating= 1.0 Rating= 5.8
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Previous Methods
Calculate the correlation of the two radiologists’ ratings= 0.785
Calculated correlation coefficient of NMI values and radiologist’s ratings Correlation = 0.649
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Good Rating: 5, Bad MI: 1.135
• Clear difference between the two images, not due to misregistration but to interval change
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Good Rating: 5, Bad MI: 1.135
• Clear difference between the two images, not due to misregistration but to interval change
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Motivation for New Data•Calculate the NMI on portions of the bottom removed
•Pathologic change•Positioning of the body affects diaphragm •Inaccurately defining inferior bottom ribcages
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Previous Key Results
0.500
0.550
0.600
0.650
0.7000.750
0.800
0.850
0.900
0.950
1.000
0% 10% 20% 30% 40% 50% 60%Percent Lung Mask Cropped
Co
rre
lati
on Full Resolution
256 Gray Levels
128 Gray Levels
64 Gray Levels
32 Gray Levels
• Maximum correlation = 0.785
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New Methods Same radiologist
rated left and right lungs on subtraction image separately
Calculate NMI on right and left lung
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Results- Correlation Coefficient
Right max: 0.746 Left max: 0.752 Average max: 0.782
Averaged Lung NMI with Averaged Rating
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30 40 50 60
Percent Cropped
Co
rre
lati
on
Co
eff
icie
nt full resolution
256 gray levels
128 gray levels
64 gray levels
32 gray levels
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New Methods Randomly divide 69 patients into 2 sets
Training Set: 34 patients, ~66.5 pairs of images
Testing Set 35 patients, ~71.5 pairs of images 20 different trials Calculated NMI on all 35 combinations of
parameters of testing set Determine correlation coefficient Choose 3 trials with maximum correlation
coefficient
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New Method
Apply these parameters to testing sets and calculate: Correlation coefficient Calculate predicted rating
Use regression line from training set and substitute MI value from testing set
ROC analysis
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Results
0% 10% 20% 30% 40% 50% 60%
Full Resolution - - - - - - -
256 Gray Levels - - - - - - -
128 Gray Levels - - - - - 2 3
64 Gray Levels - 1 - - 2 10 10
32 Gray Levels - 1 1 - 4 11 15
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Results
Sensitivity = TP/(TP + FN)
Specificity = TN/(TN+FP)
TP = Calculated Rating < 3, True Rating < 3
TN = Calculated Rating ≥ 3, True Rating ≥ 3
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Results
Specificity SensitivityCorrelation Coefficient
Max 0.936 0.850 0.864
Min 0.696 0.440 0.632
Average 0.851 0.667 0.785
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ResultsConventional Binormal ROC
Curves
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.5 1False Positive Fraction
Tru
e P
osi
tive
Fra
ctio
n
60%, 32 GrayLevels, Az = 0.909
60%, 32 GrayLevels, Az= 0.900
50% 32 GrayLevels, Az= 0.915
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New Method Calculate normalized cross-correlation to
compare usefulness of MI technique
Only compute 1 cross-correlation value for each pair of image when directly aligned
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Results
All cross-correlation values range from 0.999-1.0
Correlation with Radiologist’s ratings = 0.035 – 0.180
No Information Gained
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Conclusion
Successfully demonstrated a correlation between MI and radiologist evaluation
Calculating the NMI on the top 50% of the lung mask and scaling to 128 bins has a correlation of 0.785, comparable to that of the two radiologists
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Conclusion
Maximum Az value for 60 testing sets = 0.915
For training set, cropping 50% of the lung mask and scaling to 32 gray levels maximum correlation and Az
Mutual information gives complimentary information to that of cross-correlation
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Future Work
Mutual information can be incorporated into existing temporal subtraction algorithm
Calculate NMI on warped previous and current images
Determine if predicted rating < 3 Re-warp or inform radiologists
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Questions?