1. 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

2. 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

3. 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

4. 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

5. 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

6. JNDmetrix Model

7. 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

8. Physical Evaluation Luminance: 0.8 cd/m 2 – 500 cd/m 2 ) – Same on both NPS: P104 > P45 SNR: P45 > P104 Model input – Each stimulus on CRT imaged with CCD camera

9. Phosphor Granularity P45 Phosphor < P104 Phosphor

10. Monitor NPS

11. 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

12. Edited Images Original 75%  Ca++ 50%  Ca++ 25%  Ca++ 0%  Ca++

13. 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

14. 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

15. 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

16. 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

17. Human Results * * * * P < 0.05

18. Model Results * P < 0.05 * * * *

19. Correlation

20. Summary P104 – > light emission efficiency – > spatial noise due to granularity P45 – > SNR Luminance – noise tradeoff P45 > P104 detection performance JNDmetrix model predicted well

21. 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