mutual information as a measure for image quality of temporally subtracted chest radiographs...
Post on 20-Dec-2015
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Mutual Information as a Measure for Image
Quality of Temporally Subtracted Chest
Radiographs
Samantha PassenSamuel G. Armato III, Ph.D.
Introduction Commonly, radiologists compare multiple
chest radiographs side-by-side
Current Previous
Introduction Kano et al. (1994)- Temporal Subtraction
Detect ribcage edges and denote “lung mask”
Current
Introduction ROIs involved in nonlinear geometric warping
to align previous image to current
Current Warped Previous
Introduction Temporal Subtraction
Image
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
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
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
Mutual Information (MI) Joint Histogram:
Lower Entropy------------------Higher Entropy
(misregistration) Mutual Information:
Materials Radiologists rated 138 temporal
subtraction images from 1.0-5.9
Rating= 1.0 Rating= 5.8
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
Good Rating: 5, Bad MI: 1.135
• Clear difference between the two images, not due to misregistration but to interval change
Good Rating: 5, Bad MI: 1.135
• Clear difference between the two images, not due to misregistration but to interval change
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
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
New Methods Same radiologist
rated left and right lungs on subtraction image separately
Calculate NMI on right and left lung
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
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
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
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
Results
Sensitivity = TP/(TP + FN)
Specificity = TN/(TN+FP)
TP = Calculated Rating < 3, True Rating < 3
TN = Calculated Rating ≥ 3, True Rating ≥ 3
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
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
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
Results
All cross-correlation values range from 0.999-1.0
Correlation with Radiologist’s ratings = 0.035 – 0.180
No Information Gained
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
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
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
Questions?