Human Vision Model to Predict Observer

Performance: Detection of Microcalcifications as a

Function of Monitor Phosphor

Elizabeth Krupinski, PhDJeffrey Johnson, PhDHans Roehrig, PhDJeffrey 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

sa mpling

proba bility

distance metric

optic s

Q norm

JN Dva lue

input images

frequency specificcontrastpyramid

oriented responses

transducerMasking - gain control

JNDMap

...

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

0.00 20.00 40.00 60.00Spatia l F requency (lp /m m )

10.00

100.00

1000.00

10000.00

NPS

P104: R atio 4

P45: R atio 4

N yquist F requency of the C R T under test (3 .5 lp /m m )

Raste r F requency6.9 lp /m m

N PS of tw o S iem ens M onitors for ADU 127, one w ith a P104 phosphor, and one w ith a P45 phosphor.The data were norm a- lized to a C CD exposure of 10,000 AD U . Three CC D to C R T p ixel ratios w ere used: 53:1, 8:1 and 4 :1.

P104:R atio 8

P 45:R atio 8

P104:R atio 53

P45: R a tio 53

Images• Mammograms USF Database • 512 x 512 sub-images extracted• 13 malignant & 12 benign Ca++

• Removed using median filter • Add Ca++ to 25 normals• 75%, 50% & 25% contrasts by

weighted superposition of signal-absent & present versions

• 250 total images • Decimated to 256 x 256

Edited Images

Original 75% Ca++ 50% Ca++

25% Ca++ 0% Ca++

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 512 256 x 256 1 47.5% 46% 2 57% 47.5% 3 39% 49.5% Average 47.83%

sd = 7.35 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 Ca++ present or absent•Rate confidence 6-point scale•Multi-Reader Multi-Case Receiver

Operating Characteristic*

* Dorfman, Berbaum & Metz 1992

Human Results

00.10.20.30.40.50.60.70.80.9

1

Mea

n Az

25%

50%

75%

100%

Over

all

P104P45

* * *

* P < 0.05

Model Results

02468

101214

JND

25% 50% 75% 100%

P104P45

* P < 0.05

**

* *

Correlation

R2 = 0.973

0.5

0.6

0.7

0.8

0.9

1.0

5 7 9 11 13 15

Model JND

Rad

iolo

gist

s' M

ean

Az

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