Abstract

We compared the ability of three model observers (nonprewhitening matched filter with an eye filter, Hotelling and channelized Hotelling) in predicting the effect of JPEG and wavelet-Crewcode image compression on human visual detection of a simulated lesion in single frame digital x-ray coronary angiograms. All three model observers predicted the JPEG superiority present in human performance, although the nonprewhitening matched filter with an eye filter (NPWE) and the channelized Hotelling models were better predictors than the Hotelling model. The commonly used root mean square error and related peak signal to noise ratio metrics incorrectly predicted a JPEG inferiority. A particular image discrimination/perceptual difference model correctly predicted a JPEG advantage at low compression ratios but incorrectly predicted a JPEG inferiority at high compression ratios. In the second part of the paper, the NPWE model was used to perform automated simulated annealing optimization of the quantization matrix of the JPEG algorithm at 25:1 compression ratio. A subsequent psychophysical study resulted in improved human detection performance for images compressed with the NPWE optimized quantization matrix over the JPEG default quantization matrix. Together, our results show how model observers can be successfully used to perform automated evaluation and optimization of diagnostic performance in clinically relevant visual tasks using real anatomic backgrounds.

© 2002 Optical Society of America

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    [CrossRef]
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    [CrossRef]

Academic Radiology

M.P. Eckstein, J.S. Whiting. �??Lesion detection in structured noise,�?? Academic Radiology 2, 249-253 (1995).
[CrossRef] [PubMed]

Am. J. Card.

V. H. Rigolin, P. A. Robiolio, L.A. Spero, B.P. Harrawood, K.G. Morris, D.F. Fortin, W.A. Baker, T.M. Bashore, and J.T. Cusma, �??Compression of Digital Coronary Angiograms Does Not Affect Visual or Quantitative Assessment of Coronary Artery Stenosis Severity,�?? Am. J. of Card. 78, 131-135 (1996).
[CrossRef]

British J. Radiology

P.F. Judy, R.G. Swensson. �??Detection of small focal lesions in CT images: effects of reconstruction filters and visual display windows,�?? British J. Radiology 58, 137-145 (1985).
[CrossRef] [PubMed]

Circ.

W.A. Baker et al., �??Lossy (15:1) JPEG compression of digital coronary angiograms does not limit detection of subtle morphological features,�?? Circ. 96, 1157-1164 (1997).
[CrossRef]

Digest of Tech. Papers

H.A. Peterson, A.J. Ahumada, A.B. Watson. �??The visibility of DCT quantization noise, Soc. For Information Display,�?? Digest of Tech. Papers 24, 942-945 (1993).

A.B. Watson, �??DCTune: A Technique for visual optimization of DCT quantization matrices for individual images, Soc. For Information Display,�?? Digest of Tech. Papers XXIV, 946-949 (1993).

IEEE Data Compression Conference

A. Zandi, J. Allen, E.L. Schwartz, M. Boliek, �??Crewcode Lossless/Lossy Medical Image Compression,�?? IEEE Data Compression Conference, 212-221 (1995).

Int. J. Cardiology

S. Silber, R. Dorr, G. Zindler, H. Muhling, T. Diebel, �??Impact of various compression rates on interpretation of digital coronary angiograms,�?? Int. J. Cardiology 60, 195-200 (1997).
[CrossRef]

J. Am. College Cardiology

R.A. Kerensky, J.T. Cusma, P. Kubilis, R. Simon, T.M. Bashore, J.W. Hirshfeld, D.R. Holmes Jr, C.J. Pepine, S.E. Nissen, �??American College of Cardiology/European Society of Cardiology International Study of Angiographic Data Compression Phase I: The effect of lossy data compression on recognition of diagnostic features in digital coronary angiography,�?? J. Am. College Cardiology 35, 1370-1379 (2000).
[CrossRef]

J. Opt. Soc. Am.

M.P. Eckstein, C.K. Abbey, F.O. Bochud. �??Visual signal detection in structured backgrounds IV. Figures of merit for model observers with internal response correlations,�?? J. Opt. Soc. Am. 17, 2 206-217 (2000).
[CrossRef]

J. Opt. Soc. Am. A

J. Opt. Soc. Am. A.

A.E. Burgess, X. Li, C.K. Abbey. �??Visual signal detectability with two noise components: anomalous masking effects,�?? J. Opt. Soc. Am. A 14, 2420-2442 (1997).
[CrossRef]

J. Opt. Soc. of Am. A

C.K. Abbey, H.H. Barrett. �??Human and model-observer performance in ramp-spectrum noise: effects of regularization and object variability,�?? J. Opt. Soc. of Am. A 18, 473-488 (2001).
[CrossRef]

Math. Methods Med. Imaging

J. Yao, H.H. Barrett. �??Predicting human performance by a channelized Hotelling observer model,�?? Math. Methods Med. Imaging, SPIE 1768, 161-168 (1992).

Med. Phys.

A.E. Burgess, F.L. Jacobson, P.F. Judy. �??Human observer detection experiments with mammograms and power-law noise,�?? Med. Phys. 28, 419-437 (2001).
[CrossRef] [PubMed]

B. Zhao, L.H. Schwarz, P.K. Kijewski. �??Effect of lossy compression on lesion detection: Predictions of the nonprewhitening matched filter,�?? Med. Phys. 25, 1621-1624 (1998).
[CrossRef] [PubMed]

Opt. Express

OSA Annual Meeting Tech. Dig.

J.S. Whiting, M.P. Eckstein, S. Einav, N.L. Eigler, �??Perceptual Evaluation of JPEG compression for medical image sequences,�?? in OSA Annual Meeting Tech. Dig. 23, 161 (1992).

Percept. Psychophys.

R.G. Swensson, P.F. Judy. �??Detection of noisy visual targets: model for the effects of spatial uncertainty and signal to noise ratio,�?? Percept. Psychophys. 29: 521-534 (1981).
[CrossRef] [PubMed]

Proc SPIE

K.K. Chan, C.C. Lau, S.L. Lou, A. Hayrepatian, B.K.T. Ho, H.K. Huang, �??Three-dimensional Transform Compression of Image from Dynamic Studies,�?? Medical Imaging IV: Image Capture and Display, Proc SPIE 1232, 322-326 (1990).
[CrossRef]

Proc. Natl. Acad. Sci.

H.H. Barrett, J. Yao, J.P. Rolland, K.J. Myers, �??Model observers for assessment of image quality,�?? Proc. Natl. Acad. Sci. USA 90, 9758- 9765 (1993).
[CrossRef] [PubMed]

Proc. SPIE

S.C. Lo, E.L. Shen, K.M. Seong, �??An image splitting and remapping method for radiological image compression,�?? Medical Imaging IV: Image Capture and Display, Proc. SPIE 1232, 312 �?? 321 (1990).
[CrossRef]

M. Goldberg, S. Panchanathan, L.A. Wang, �??Comparison of Lossy Techniques for Digitized Radiographic Images,�?? Medical Imaging IV: Image Capture, Formatting and Display, Proc. SPIE 1987, 269-281 (1993).

C.K. Abbey, H.H. Barrett, M.P. Eckstein. �??Practical issues and methodology in assessment of image quality using model observers,�?? in Medical Imaging, H. Roerhig, ed., Proc. SPIE, The physics of medical imaging, 3032: 182-194 (1997).
[CrossRef]

R.F. Wagner, K.E. Weaver. �??An assortment of image quality indices for radiographic film-screen combinations- can they be resolved?�?? In Application of Optical Instrumentation in Medicine I, P.L. Carson, WH Hendee, and WC Zarnstorff, eds, Proc. SPIE 35, 83-94 (1972).
[CrossRef]

M.P. Eckstein, C.A. Abbey, J.S. Whiting. �??Human vs model observers in anatomic backgrounds,�?? Proceedings SPIE Image Perception 3340, 15-26 (1998).

C.K. Abbey, H.H. Barrett, D.W. Wilson. �??Observer signal to noise ratios for the ML-EM algorithm,�?? Proc. SPIE 2712, 47-58 (1996).
[CrossRef]

H.H. Barrett, C.K. Abbey, B. Gallas, M.P. Eckstein. �??Stabilized estimates of Hotelling-observer detection performance in patient structured noise,�?? Proc. SPIE 3340 (1998).
[CrossRef]

M.P. Eckstein, C.K Abbey. �??Model observers for signal known statistically tasks,�?? Proc. SPIE, Medical Imaging, Image Percep. and Performance, Ed. E.A. Krupinski, D.P. Chakraborty 4321, 91-102 (2001).
[CrossRef]

M.P. Eckstein, C.K. Abbey, B. Pham. �??The effect of image compression on signal known statistically tasks,�?? Proc. SPIE, Medical Imaging, Image Percep . and Performance, Ed. E.A. Krupinski, D.P. Chakraborty 4686, 13-24 (2002).
[CrossRef]

A.J. Ahumada, Jr., A.B. Watson, A.M. Rohally. "Models of human image discrimination predict object detection in natural backgrounds," in Human Vision, Visual Proc., and Digital Display VI, ed. B. Rogowitz and J. Allebach , SPIE 2411, 355-362 (1995).
[CrossRef]

J.P. Johnson, J. Lubin, J. Nafziger, D. Chakraborty. �??Visual Discrimination Modeling of lesion discriminability,�?? Medical Imaging , Image Percep.and Performance, Ed. E.A. Krupinski, D.P. Chakraborty, Proc. SPIE, 4686, 248-255 (2002).
[CrossRef]

Radiology

M. Ishida, K. Doi, L.N. Loo, C.E. Metz, J.L. Lehr. �??Digital image processing: effect of detectability of simulated low-contrast radiographic patterns,�?? Radiology 150, 569-575 (1984).
[PubMed]

Science

A.E. Burgess, R.B. Wagner, R.J. Jennings, and H.B. Barlow, �??Efficiency of human visual signal discrimination,�?? Science 214, 93-94 (1981).
[CrossRef]

Other

J. Lubin, �??The use of psychophysical data and models in the analysis of display system performance,�?? in Digital images and human vision, Ed. A.B. Watson, (MIT Press, 1993) 163-178.

S. Daly, �??The visible differences predictor: an algorithm for the assessment of image fidelity,�?? in Digital images and Human Vision, A.B. Watson, ed. (MIT Press, Cambridge, Mass., 1993) 162-178.

A.B. Watson, A.P. Gale, J.A. Solomon, A.J. Ahumada. �??Visibility of DCT quantization noise: Effects of display resolution," Proceedings, Society for Information Display, San Jose, CA, Society for Information Display, 697-700 (1995).

M.P. Eckstein, C.K. Abbey, F.O.Bochud. �??Practical guide to model observers in synthetic and real noisy backgrounds,�?? in Handbook of Medical Imaging Vol. I: Physics and Psychophysics, Editors, J. Beutel, Kundel, Van Metter, SPIE Press, 593-628 (2000)

A.B. Watson. Detection and recognition of simple spatial forms, in Physical and Biological Processing of Images, O.J. Bradick & A.C. Sleigh, Eds. (New York, Springer-Verlag, 1983).

F.O. Bochud, C.K. Abbey, M.P. Eckstein. �??Correlated human responses for visual detection in natural images; Annual Meeting of the Association for Research,�?? in Vision and Ophthalmology; Fort Lauderdale, USA; 40, 4; 350 (1999).

D.M. Green, J.A. Swets. Signal Detection Theory and Psychophysics, (Wiley, NewYork, 1966)

W.B. Pennebaker, J.L. Mitchell, The JPEG still image data compression standard, (Van Nostrand Reinhold, New York, 1993).

W.E. Smith. �??Simulated annealing and estimation theory in coded aperture imaging,�?? PhD Dissertation, University of Arizona (2002).

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Figures (5)

Fig. 1.
Fig. 1.

Human (red squares for JPEG, blue triangles for wavelet Crewcode) vs. model performance (empty squares and continuous line). Different rows are for different model observes (NPWE; Non-Prewhitening matched filter with an eye filter, CH-Hot: channelized Hotelling; HOT: Hotelling) as a function of compression ratio. Top line is for JPEG and bottom line is for wavelet Crewcode. Left and right columns non-physician observers: GR and CH.

Fig. 2.
Fig. 2.

Physician (red symbols, JPEG; blue symbols wavelet-Crewcode) vs. model observer performance. Different panels are for different model observers as a function of compression ratio.

Fig. 3.
Fig. 3.

Left: Root mean square error between original and image undergoing different degrees of compression (averaged across the 424 test images) for the JPEG (red squares) and wavelet-Crewcode algorithms (blue triangles). Right: DC-tune 2.0 metric as a function of image compression for the JPEG and wavelet-Crewcode algorithms.

Fig. 4:
Fig. 4:

Simulated Annealing procedure applied to the optimization of the JPEG quantization matrix based on model observer performance following Smith (1985)

Fig. 5.
Fig. 5.

Top left: Performance of the NPWE model for three different quantization matrices. Top right: Performance for NPWE model across compression ratios for the default quantization matrix and the 25:1 optimized quantization matrix. Bottom left: Performance for observer GR for three quantization matrices. Bottom right: Performance for physician observer DV for three quantization matrices

Tables (2)

Tables Icon

Table 1. Goodness of fit assessed with a reduced Chi-square (χr2), for the three models to individual data for JPEG, Crewcode-wavelet and pooled across both algorithms and observers. Highlighted numbers correspond to lowest reduced chi-square value within a condition. Reduced Chi-squared is defined as χ r 2 = 1 n p i = 0 n ( d h , i ' d m , i ' ) 2 σ i 2 , where d’h,i is for the human in the ith condition, d’m,i is for the model, σi2 is the observed variance of the human d’h,i, n is the number of data points (n=10) and p is the number of fitting parameters (p=1).

Tables Icon

Table 2. Quantization matrix for the default JPEG standard

Equations (14)

Equations on this page are rendered with MathJax. Learn more.

λ m = x = 1 N y = 1 N w x y g m x y
λ m = w t g m
w x y = FFT 1 [ s u v E u v 2 ]
E ( f ) = f ρ exp ( - cf γ )
w h = K - 1 [ < g s > - < g b > ]
V x y = exp [ 4 ln 2 ( x 2 + y 2 ) W s 2 ] cos [ 2 π f c ( x cos θ + y sin θ ) + β ]
b w = log 2 [ f c + 1 2 W f f c 1 2 W f ]
a = K V - 1 [ < g V s > - < g V b > ]
w x y = i = 0 N a i · V i x y
λ m = λ m , e + ε m
P ̂ c = 1 J j = 1 J step ( λ s , j max i ( λ b , ij ) )
Pc d mafc M = + φ ( z d mafc ) [ ϕ ( z ) ] M 1 dz
RMSE = 1 XY x = 0 X y = 0 Y [ I x y I c x y ] 2
c ' i , j = round ( c i , j q i , j )

Metrics