Transcript Optimizing Softcopy Mammography Displays Using a Human

Human Vision Model to Predict
Observer Performance:
Detection of
Microcalcifications as a
Function of Monitor Phosphor
Elizabeth Krupinski, PhD
Jeffrey Johnson, PhD
Hans Roehrig, PhD
Jeffrey Lubin, PhD
Michael Engstrom, BS
Acknowledgments
This work was supported by a
grant from the NIH R01 CA
87816-01. We would also like
to thank Siemens for the loan
of 1 of the monitors and
MedOptics for 1 of the CCD
cameras used in the study
Rationale
• Digital mammography potential
– Improve breast cancer detection
– CAD does not need digitization
• Display monitors should be optimized
– Physical evaluation parameters
– Psychophysical evaluation (JNDs)
– Clinical evaluation radiologists
Rationale
• Observer trials (ROC studies)
– Require many images (power)
– Require many observers (power)
– Are time-consuming
• Predictive models may help
– Simulate effects softcopy display
parameters on image quality
– Predict effects on performance
JNDmetrix Model
• Computational method predicting
human performance in detection,
discrimination & image-quality tasks
• Based on JND measurement
principles & frequency-channel
vision-modeling principles
• 2 input images & model returns
accurate, robust estimates of visual
discriminability
JNDmetrix Model
input images
ori ented r esponses
optic s
tr ansducer
Masking - gain control
sa m pli ng
...
distance m etri c
Q norm
JN D
va lue
fr equency
specific
contrast
pyr amid
JND
Map
proba bility
Display Monitors
• 2 Siemens high-performance
– 2048 x 2560 resolution
– Dome MD-5 10-bit video board
– 71 Hz refresh rate
– Monochrome
– Calibrated to DICOM-14 standard
• P45 vs P104 phosphor
Physical Evaluation
• Luminance: 0.8 cd/m2 – 500 cd/m2)
– Same on both
• NPS: P104 > P45
• SNR: P45 > P104
• Model input
– Each stimulus on
CRT imaged with CCD camera
Phosphor Granularity
P45 Phosphor
<
P104 Phosphor
Monitor NPS
10000.00
NPS of two Siemens Monitors for ADU 127,
one with a P104 phosphor, and one with a
P45 phosphor.The data were normalized to a CCD exposure of 10,000 ADU.
Three CCD to CRT pixel ratios were
used: 53:1, 8:1 and 4:1.
P104:
Ratio 8
1000.00
NPS
P104:
Ratio 53
P104:
Ratio 4
P45:
Ratio 4
100.00
P45:
Ratio 8
P45:
Ratio 53
Nyquist Frequency
of the CRT under
test (3.5 lp/mm)
Raster Frequency
6.9 lp/mm
10.00
0.00
20.00
40.00
Spatial Frequency (lp/mm)
60.00
Images
•
•
•
•
•
•
Mammograms USF Database
512 x 512 sub-images extracted
13 malignant & 12 benign mCa++
Removed using median filter
Add mCa++ to 25 normals
75%, 50% & 25% contrasts by
weighted superposition of signalabsent & present versions
• 250 total images
• Decimated to 256 x 256
Edited Images
Original
75% mCa++
25% mCa++
50% mCa++
0% mCa++
Image Editing Quality
• 512 x 512 & 256 x 256 versions
• 200 pairs of images
– Original contrast only
– Paired with edited version
– Paired randomly with others
• 3 radiologists
• 2AFC – chose which is edited
Editing Quality Results
Reader
512 x 512
1
47.5%
46%
2
57%
47.5%
3
39%
49.5%
Average
47.83%
sd = 7.35
256 x 256
47.67%
sd = 1.08
Observer Study
• 250 images
– 256 x 256 @ 5 contrasts
• 6 radiologists
• No image processing
• Ambient lights off
• No time limits
• 2 reading sessions ~ 1 month apart
• Counter-balanced presentation
Observer Study
• Images presented individually
• Is mCa++ present or absent
• Rate confidence 6-point scale
• Multi-Reader Multi-Case Receiver
Operating Characteristic*
* Dorfman, Berbaum & Metz 1992
*
*
*
Overall
100%
75%
P104
P45
50%
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
25%
Mean Az
Human Results
* P < 0.05
Model Results
14
12
*
JND
10
8
6
*
*
*
P104
P45
4
2
0
25%
50%
75%
100%
* P < 0.05
Radiologists' Mean Az
Correlation
1.0
0.9
0.8
0.7
R2 = 0.973
0.6
0.5
5
7
9
11
Model JND
13
15
Summary
• P104
– > light emission efficiency
– > spatial noise due to granularity
• P45
– > SNR
• Luminance – noise tradeoff
• P45 > P104 detection performance
• JNDmetrix model predicted well
Model Additions
• Eye-position will be recorded as
observers search images to
determine if any attention
parameters can be added to
JNDmetrix model to improve
accuracy of predictions