Effects of Grayscale Window/Level on Breast Lesion Detectability Jeffrey Johnson, PhD a John...

Effects of Grayscale Window/Level on Breast

Lesion DetectabilityJeffrey Johnson, PhD a

John Nafziger, PhD a Elizabeth Krupinski, PhD b

Hans Roehrig, PhD b

baSupported by U. S. Army Medical Research and Materiel Command, grant DAMD-17-01-1-0621

2

Rationale

Nearly 50% of breast lesions missed at initial screening are visible retrospectively

Digital mammography could reduce perceptual errors by enhancing lesion conspicuity with image processing

Perceptual models could be useful tools for automating and optimizing techniques for image enhancement

3

Overview This study evaluated the use of a visual

discrimination model (VDM) for predicting effects of one type of image enhancement - grayscale window width and level (W/L) - on the detectability of breast lesions

Compared model and observer performance in two experiments:– 2AFC detection thresholds with simulated

mammograms and nonmedical observers– ROC observer performance study with radiologists

and digitized mammograms

4

Methods: Simulated Mammograms

Backgrounds– Filtered noise, 1/f3

noise power spectrum– Two groups: Bright and

Dark central regions

Lesion signals– Mass: 2D Gaussian (d=50

arcmin)– Microcalcification cluster:

six blurred disks or “specks” (disk d=8 arcmin, cluster d=40 arcmin)

5

Methods: W/L Conditions P-value transformations:

– Fully stretched– Understretched (-25%)– Overstretched (±25%)– Bright shifted (+25%)– Dark shifted (-25%)

Applied to full 512x512 pixel image or 170x170 pixel central region of interest containing lesion

0

256

512

768

1024

0 1024 2048 3072 4096p

p'

Fully stretchedUnderstretched (25%)Overstretched (25%)Bright shifted (25%)Dark shifted (25%)

6

Example Test Images

Fullystretched

(FS)

Understretched

(US)

Overstretched

(OS)

GaussianFull W/L

Bright Center

GaussianCentral W/L

Bright Center

SpecksFull W/L

Dark Center

SpecksCentral W/LDark Center

7

Example Test Images

Bright shifted(BS)

Dark shifted(DS)

GaussianFull W/L

Bright Center

GaussianCentral W/L

Bright Center

SpecksFull W/L

Dark Center

SpecksCentral W/LDark Center

8

2AFC Threshold Detection

Side-by-side presentation of same background with/without signal

Signal amplitude varied in 1-up/3-down staircase procedure; detection threshold at ~80% correct

Five W/L conditions interleaved in same session

Separate sessions for two signal and two background types

9

Test Conditions Siemens 5M-pixel CRT monitor (P45) Luminance range = 0.3 to 290 cd/m2

Barco 10-bit display controller DICOM-14 grayscale display function Three nonmedical observers Viewing distance = 52 cm; chin rest Ambient lights off

10

Results: Detection Thresholds for Gaussian Signals

0.00

0.03

0.06

0.09

0.12

FullyStretched

UnderStretched

OverStretched

BrightShifted

DarkShifted

W/L Method

Det

ectio

n Th

resh

old

Full W/LCentral W/L

Gaussian on Bright Filtered Noise

0.00

0.03

0.06

0.09

0.12

FullyStretched

UnderStretched

OverStretched

BrightShifted

DarkShifted

W/L Method

Det

ectio

n Th

resh

old

Full W/LCentral W/L

Gaussian on Dark Filtered Noise

Error bars show 95% confidence intervals

Bright Backgrounds Dark Backgrounds

11

Results: Detection Thresholds for Speck Clusters

Error bars show 95% confidence intervals

0.00

0.03

0.06

0.09

0.12

0.15

FullyStretched

UnderStretched

OverStretched

BrightShifted

DarkShifted

W/L Method

Det

ectio

n Th

resh

old

Full W/LCentral W/L

Specks on Bright Filtered Noise

0.00

0.03

0.06

0.09

0.12

0.15

FullyStretched

UnderStretched

OverStretched

BrightShifted

DarkShifted

W/L Method

Det

ectio

n Th

resh

old

Full W/LCentral W/L

Specks on Dark Filtered Noise

Bright Backgrounds Dark Backgrounds

12

Experimental Detection Thresholds

Significant variations across W/L conditions Generally lower for central vs. full W/L

– due to local contrast enhancement- fully stretched not always optimal

Full W/L: Lowest thresholds for …– fully stretched, understretched (specks only)– dark shifted on bright, bright shifted on dark

Central W/L: Lowest thresholds for …– overstretched for Gaussians and specks on dark– dark shifted on bright, bright shifted on dark

13

Visual Discrimination Modeling

Simulates physiological response of human visual system to visual stimuli: luminance patterns from images & video

Output is a deterministic prediction of feature or image discriminability as function of spatial location, spatial frequency, and time

Discriminability measured in units of Just Noticeable Differences (JND)

14

VDM Architecture

…

JND scalar

Spatial frequencybands

Spatial orientation responses

Display& Ocular

Processing

Optics

Crossband Masking

JND map

JND Distance

Combin. Rule

Display luminance

Pair of input images

Probability

Contrast Pyramid (visual cortex)

Within-band Masking

Contrast Pyramid

…

15

VDM vs. Experimental Thresholds for Gaussians on Bright

Backgrounds

0.00

0.03

0.06

0.09

0.12

FullyStretched

UnderStretched

OverStretched

BrightShifted

DarkShifted

W/L Condition

Con

tras

t Thr

esho

ld

ExperimentalModel

Gaussian on BrightFiltered Noise: Full W/L

Error bars show 95% confidence intervals

0.00

0.03

0.06

0.09

0.12

FullyStretched

UnderStretched

OverStretched

BrightShifted

DarkShifted

W/L Condition

Con

tras

t Thr

esho

ld

ExperimentalModel

Gaussian on BrightFiltered Noise: Central W/L

Full W/L Central W/L

16

VDM vs. Experimental Thresholds for Gaussians on Dark Backgrounds

Error bars show 95% confidence intervals

0.00

0.03

0.06

0.09

0.12

FullyStretched

UnderStretched

OverStretched

BrightShifted

DarkShifted

W/L Condition

Con

tras

t Thr

esho

ld

ExperimentalModel

Gaussian on DarkFiltered Noise: Full W/L

0.00

0.03

0.06

0.09

0.12

FullyStretched

UnderStretched

OverStretched

BrightShifted

DarkShifted

W/L Condition

Con

tras

t Thr

esho

ld

ExperimentalModel

Gaussian on DarkFiltered Noise: Central W/L

Full W/L Central W/L

17

VDM vs. Experimental Thresholds for Specks on Bright Backgrounds

Error bars show 95% confidence intervals

0.00

0.04

0.08

0.12

0.16

FullyStretched

UnderStretched

OverStretched

BrightShifted

DarkShifted

W/L Condition

Con

tras

t Thr

esho

ld

ExperimentalModel

Specks on BrightFiltered Noise: Full W/L

0.00

0.04

0.08

0.12

0.16

FullyStretched

UnderStretched

OverStretched

BrightShifted

DarkShifted

W/L Condition

Con

tras

t Thr

esho

ld

ExperimentalModel

Specks on BrightFiltered Noise: Central W/L

Full W/L Central W/L

18

VDM vs. Experimental Thresholds for Specks on Dark Backgrounds

Error bars show 95% confidence intervals

0.00

0.04

0.08

0.12

0.16

FullyStretched

UnderStretched

OverStretched

BrightShifted

DarkShifted

W/L Condition

Con

tras

t Thr

esho

ld

ExperimentalModel

Specks on DarkFiltered Noise:

Full W/L

0.00

0.04

0.08

0.12

0.16

FullyStretched

UnderStretched

OverStretched

BrightShifted

DarkShifted

W/L Condition

Con

tras

t Thr

esho

ld

ExperimentalModel

Specks on DarkFiltered Noise: Central W/L

Central W/LFull W/L

19

VDM vs. Experimental Thresholds: Simulated Lesions & Backgrounds

Generally good agreement between model and experimental detection thresholds and variations across W/L conditions

Consistently reduced thresholds with central (local ROI) vs. full-image W/L

Largest modeling discrepancies for specks, especially on dark backgrounds

20

ROC Observer Study

Determine effects of W/L functions and size on detection of microcalcification clusters by mammographers

Evaluate utility of localized ROI contrast enhancement (central vs. full W/L)

21

ROC Observer Study: Image Preparation

Digitized mammograms (n=15) from Digital Database for Screening Mammography

Extracted 512x512-pixel sections with single, centered microcalcification cluster

Removed calcifications by median filtering Generated five lesion-contrast levels: 0, 25, 50, 75,

and 100% Applied three W/L functions: Fully stretched, under

and over stretched by 15% Full and Central W/L sizes

22

ROC Observer Study: Test Conditions

6 radiologists at Univ. of Arizona 225 images/session 2 reading sessions ~2 weeks apart Decision confidence on 6-point scale No image processing, no time limits,

ambient lights off; viewed at ~25 cm Siemens 5M-pixel CRT monitor (P45) Luminance = 0.8 to 500 cd/m2

DICOM-14 grayscale display function

23

Examples of Test ImagesUnderstretched

(US, 15%) Overstretched

(OS, 15%) Fully stretched(FS, 0-4095)

FullW/L

CentralW/L

24

ROC Observer Study: Results Compared central vs. full W/L across all W/L functions, all lesion

contrasts Observer performance statistically better (p<0.05) for FULL W/L size

Az Values

25

ROC Observer Study: Results No statistically significant variations:

– between central and full W/L sizes for a single W/L function (all lesion contrasts)– between central and full W/L sizes for a single combination of W/L function and lesion contrast (except FS,

50%)– across W/L functions in central and full W/L sizes considered separately (all lesion contrasts)

26

ROC Observer Study: Analysis Central W/L enhanced lesion contrast but changed appearance of parenchymal tissue relative to surrounding

areas Decision confidence lowered by nonuniform appearance of background tissue characteristics Conclusion: Calcifications may be easier to perceive (due to higher contrast) but more difficult to interpret (due

to cognitive factors, past experience)

27

Conclusions For simulated lesions and backgrounds, VDM was generally a reliable predictor of W/L conditions for optimal detectability

Results with simulated images suggested benefits of localized contrast enhancement

Decision confidence and performance of mammographers actually lower with localized W/L, probably due to nonuniform tissue appearance

28

Future Directions Allow toggling between full and local W/L modes (combine uniform contextual data with local contrast enhancement) Evaluate effects of W/L on detection of very subtle lesions (low contrast, near threshold) Model refinements:

– improved crossband masking for higher frequency signals: specks/calcifications– include effects of background noise via statistical observer model