Elizabeth Krupinski, PhD Jeffrey Johnson, PhD Hans Roehrig, PhD Jeffrey Lubin, PhD

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/m2 – 500 cd/m2) • 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 mCa++ • Removed using median filter • Add mCa++ 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% mCa++ 50% mCa++ 25% mCa++ 0% mCa++

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

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 mCa++ 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