MTF Correction for Optimizing Softcopy Display of Digital

Mammograms: Use of a Vision Model for Predicting Observer

Performance

Elizabeth Krupinski, PhD1 Jeffrey Johnson, PhD2

Hans Roehrig, PhD1 Jeffrey Lubin, PhD2

Michael Engstrom, BS1

1University of Arizona 2Sarnoff CorporationThis work was supported by a grant from the

NIH R01 CA 87816-01.

Rationale• MTF (Modulation Transfer Function) of

monitors is inferior to radiographic film• In both vertical & horizontal directions

MTF is degraded (spatial resolution lost) & moreover is non-isotropic– Horizontal by ~ 10 – 20%– Vertical by ~ 30 – 40%

• Over half the contrast modulation is lost at highest spatial frequencies

• Images are thus degraded both in spatial & contrast resolution

• Maybe image processing can help !

Rationale• Observer trials (ROC) are ideal for

evaluation, but for good statistical power– Require many images – Require many observers – Often require multiple viewing

conditions– Are time-consuming

• Predictive models may help decrease need for extended & multiple ROC trials– Simulate effects of softcopy display

parameters on image quality– Predict effects on observer performance

JNDmetrix Model• Developed by the Sarnoff Corporation

– Successful in military & industrial tasks

• Computational method for predicting human performance in detection, discrimination & image-quality tasks

• Based on JND (Just Noticeable Difference) measurement principles & frequency-channel vision-modeling principles

• Uses 2 input images & the 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

...

JNDmetrix Model• Optics: input images convolved by

function approximating point spread optics of eye

• Image Sampling: by retinal cone mosaic simulated by Gaussian convolution & point-sampling sequence of operations

• Raw Luminance Image: converted to units local contrast & decomposed to Laplacian pyramid yielding 7 frequency band pass levels

• Pyramid Levels: convolved with 8 pairs spatially oriented filters with bandwidths derived from psychophysical data

JNDmetrix Model• Pairs Filtered Images: squared &

summed yielding phase-independent energy response that mimics transform in visual cortex from linear (simple cells) to energy response response (complex cells)

• Transducer Phase: energy measure each pyramid level normalized by value approximating square of frequency-specific contrast detection threshold for that level & local luminance

JNDmetrix Model• Normalized Level: transformed by

sigmoid non-linearity duplicating visual contrast discimination function

• Transducer outputs: convolved with disk-shaped kernal & averaged to account for foveal sensitivity

• Distance metric: computed from distance between vectors (m-dimensional, m = # pyramid levels x # orientations) from each spatial position

• JND Spatial Map: results representing degree discriminability; reduced to single value (Q-norm)

The Study• Measure monitor’s horizontal & vertical

MTF• Apply MTF correction algorithm

– Based on Reiker et al. Proc SPIE 1997;3035:355-368 but using a Weiner-filtering algorithm instead of the Laplacian pyramid filter

– Compensates mid to high-frequency contrast losses

• Run human observer (ROC) study– Calculate area under the curve (Az)

• Run JNDmetrix model on images– Calculate JNDs

• Compare human & model performance

Physical Evaluation• Siemens monitor: 2048 x 2560;

monochrome; P45 phosphor; Dome MD-5 video board; DICOM calibrated

• Luminance: 0.8 cd/m2 – 500 cd/m2)• Input to model: each

stimulus imaged on monitor by CCD camera to capture display effects

Block diagram of program for automatically finding the CRT MTF from a CCD image of a single CRT line

Profiles to find Vertical MTF

Profiles to find Horizontal MTF

Step 1: Input Image details like magnification, CRT pixel size and orientation of line.

Step 2: Specify ROI for profiles.

Step 3: Perform Fast Fourier Transform of the profiles and take their average.

Step 4: Correct for finite size of pixel width.

Step 5: Get a Polynomial curve fit to get normalization factor.

Step 6: Divide the average FFT by this normalization factor to obtain MTF.

CRT Line

CRT Line

0 1 2 3 4

Spatial Frequency (lp/m m)

0

0.4

0.8

1.2

MT

F

Vertical MTF:8 cd/m 2

Vertical MTF:42 cd/m 2

Vertical MTF:237 cd/m 2

Horizontal MTF:237 cd/m 2

Horizontal MTF:42 cd/m 2

Horizontal MTF:8 cd/m 2

MTFs obtained from the Line Response of a DICOMCalibrated High Performance 5M-Pixel CRT with aP45 Phosphor for Different Mean Luminances.ADUs 55,120 and 210; Nyquist Frequency: 3.47 lp/m m

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

• The Ca++ are removed using median filter • Add Ca++ to 25 normals with reduced

contrast levels– 75%, 50% & 25% Ca++ by weighted

superposition of signal-absent & present versions

• 250 total images • Decimated to 256 x 256 (for CCD imaging)

Edited Images

Original 75% Ca++ 50% Ca++

25% Ca++ 0% Ca++

MTF Restoration• If MTF is known then digital data can be

processed with essentially the inverse of the display MTF(f) before displayed:

O’(f) = O(f)/MTF(f) where O(f) is the object

• Displayed O’(f) on the monitor with MTF(f) will result in an image equivalent to the digital data O(f)

• There is no degradation and the image on CRT display looks just like digital data

I(f)=O’(f)*MTF(f)=[O’(f)/MTF(f)]*MTF(f)=O(f) (where I(f) = the displayed image)

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• Rate confidence (6-point scale)

Human ROC Results

00.10.20.30.40.50.60.70.80.9

1

Mean A

z

25% 50% 75% 100%

MTFNo MTF

* *

* P < 0.05

Model Results

0

2

4

6

8

10

12

14

JND

25% 50% 75% 100%

MTFNo MTF

* P < 0.05

**

* *

Correlation

0.6

0.7

0.8

0.9

1.0

7 8 9 10 11 12 13

Model JND

Rad

iolo

gis

ts' M

ean

Az MTF

No MTF

R2 = 0.98

Summary• MTF compensation improves

detection performance significantly• JNDmetrix model predicted human

performance well• High correlation between human &

model results• Future improvements to model may

include attention component derived from eye-position data

Model Results• Model predicted same pattern of

results as human observers– MTF processing yields higher

performance than without– At all lesion contrast levels

• Correlation between human Az and model JND is quite high