MTF Correction for Optimizing Softcopy Display of Digital Mammograms: Use of a Vision Model for Predicting Observer Performance

Elizabeth Krupinski, PhD 1 Hans Roehrig, PhD 1 Michael Engstrom, BS 1 Jeffrey Johnson, PhD 2 Jeffrey Lubin, PhD 2 1 University of Arizona 2 Sarnoff Corporation This work was supported by a grant from the NIH R01 CA 87816-01.

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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

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Horizontal by ~ 10 – 20%

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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

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Observer trials (ROC) are ideal for evaluation, but for good statistical power

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Require many images

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Require many observers

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Often require multiple viewing conditions Are time-consuming Predictive models may help decrease need for extended & multiple ROC trials

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Simulate effects of softcopy display parameters on image quality

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Predict effects on observer performance

JNDmetrix Model

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Developed by the Sarnoff Corporation

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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

input images o p tic s sa m p li n g oriented responses frequency specific contrast pyramid transducer JND Map Masking - gain control

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distance metric Q norm JN D va lu e p ro b a b ility

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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: levels converted to units local contrast & decomposed to Laplacian pyramid yielding 7 frequency band pass Pyramid Levels: convolved with 8 pairs spatially oriented filters with bandwidths derived from psychophysical data

JNDmetrix Model

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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

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JNDmetrix Model

Normalized Level: transformed by sigmoid non-linearity duplicating visual contrast discimination function Transducer outputs: shaped kernal & averaged to account for foveal sensitivity convolved with disk Distance metric: spatial position computed from distance between vectors (m-dimensional, m = # pyramid levels x # orientations) from each JND Spatial Map: degree discriminability; reduced to single value (Q-norm) results representing

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The Study

Measure monitor’s horizontal & vertical MTF Apply MTF correction algorithm

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

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Compensates mid to high-frequency contrast losses Run human observer (ROC) study

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Calculate area under the curve (Az) Run JNDmetrix model on images

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Calculate JNDs Compare human & model performance

Physical Evaluation

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Siemens monitor: 2048 x 2560; monochrome; P45 phosphor; Dome MD-5 video board; DICOM calibrated Luminance: 0.8 cd/m 2 – 500 cd/m 2 ) 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

CRT Line

Profiles to find Vertical MTF

CRT Line

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.

Profiles to find Horizontal MTF

1.2

MTFs obtained from the Line Response of a DICOM Calibrated High Performance 5M-Pixel CRT with a P45 Phosphor for Different Mean Luminances.

ADUs 55,120 and 210; Nyquist Frequency: 3.47 lp/mm

0.8

Vertical MTF: 8 cd/m 2 Vertical MTF: 237 cd/m 2 Vertical MTF: 42 cd/m 2 0.4

Horizontal MTF: 237 cd/m 2 Horizontal MTF: 8 cd/m 2 Horizontal MTF: 42 cd/m 2 0 0 1 2 Spatial Frequency (lp/mm) 3 4

Images

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Mammograms from USF Database 512 x 512 sub-images extracted 13 malignant & 12 benign

m

Ca ++ The

m

Ca ++ are removed using median filter Add

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Ca ++ to 25 normals with reduced contrast levels 75%, 50% & 25% present versions

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Ca ++ by weighted superposition of signal-absent & 250 total images Decimated to 256 x 256 (for CCD imaging)

Edited Images

Original 75%

m

Ca++ 50%

m

Ca++ 25%

m

Ca++ 0%

m

Ca++

MTF Restoration

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If MTF is known then digital data can be processed with essentially the inverse of the display MTF(f) before displayed:

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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)

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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)

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Observer Study

250 images

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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

1 0.9

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0.7

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0.5

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0.3

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0.1

0 25% * 50% * 75% MTF No MTF 100%

* P < 0.05

Model Results

2 0 6 4 14 12 10 8 * 25% * 50% * 75% * 100% MTF No MTF

* P < 0.05

Correlation

1.0

0.9

MTF No MTF R 2 = 0.98

0.8

0.7

0.6

7 8 9 10 11 Model JND 12 13

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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

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Model predicted same pattern of results as human observers

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MTF processing yields higher performance than without

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At all lesion contrast levels Correlation between human Az and model JND is quite high