Abstract

The effect of spatial noise-reduction filtering on human observer detection of stationary cylinders mimicking arteries, catheters, and guide wires in x-ray fluoroscopy was investigated in both single image frames and image sequences. Ideal edge-preserving spatial filtering was simulated by filtering of the noise before addition of the target cylinder. This allowed us to separate the effect of edge blurring from those of noise reduction and spatial noise correlation. We used three different center-weighted averagers that reduced pixel noise variance by factors of 0.75, 0.50, and 0.25. As compared with no filtering, the effect of filtering on detection in single images was statistically insignificant. This indicated an adverse effect of spatial noise correlation on detection that countered the effect of noise reduction. By comparison, spatial filtering significantly improved detection in image sequences and yielded potential x-ray dose savings of 26–34%. Comparison of results with two observer models suggested that human observers have an improved detection efficiency in spatially filtered image sequences as compared with white-noise sequences. Pixel noise reduction, a measure commonly used to assess filter performance, overestimated the effect of filtering on detection and was not a good indicator of image quality. We conclude that edge-preserving spatial filtering is more effective in sequences than in single images and that such filtering can be used to improve image quality in noisy image sequences such as x-ray fluoroscopy.

© 1999 Optical Society of America

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    [CrossRef]

1999 (1)

1998 (6)

C. van den Branden Lambrecht, M. Kunt, “Characterization of human visual sensitivity for video imaging applications,” Signal Process. 67, 255–269 (1998).
[CrossRef]

T. Matsui, “Theoretical analysis of perceptual responses to flashed sinusoidal waves using a multichannel spatiotemporal human vision model,” Electron. Commun. Jpn. Pt. 3 81, 51–62 (1998).
[CrossRef]

K. T. Blackwell, “The effect of white and filtered noise on contrast detection thresholds,” Vision Res. 38, 267–280 (1998).
[CrossRef] [PubMed]

P. Xue, C. W. Thomas, G. C. Gilmore, D. L. Wilson, “An adaptive reference/test paradigm: application to pulsed fluoroscopy perception,” Behav. Res. Methods Instrum. Comput. 30, 332–348 (1998).
[CrossRef]

P. Xue, D. L. Wilson, “Effects of motion blurring in x-ray fluoroscopy,” Med. Phys. 25, 587–599 (1998).
[CrossRef] [PubMed]

P. Xue, D. L. Wilson, “Detection of moving objects in pulsed x-ray fluoroscopy,” J. Opt. Soc. Am. A 15, 375–388 (1998).
[CrossRef]

1997 (2)

1996 (4)

D. L. Wilson, K. N. Jabri, P. Xue, R. Aufrichtig, “Perceived noise versus display noise in temporally filtered image sequences,” J. Electron. Imaging 5, 490–495 (1996).
[CrossRef]

N. H. C. Yung, A. H. S. Lai, “Performance evaluation of a feature-preserving filtering algorithm for removing additive random noise in digital images,” Opt. Eng. 35, 1871–1885 (1996).
[CrossRef]

G. Z. Yang, P. Burger, D. N. Firmin, S. R. Underwood, “Structure adaptive anisotropic image filtering,” Image Vision Comput. 14, 135–145 (1996).
[CrossRef]

P. Xue, D. L. Wilson, “Pulsed fluoroscopy detectability from interspersed adaptive forced choice measurements,” Med. Phys. 23, 1833–1843 (1996).
[CrossRef] [PubMed]

1995 (3)

T. B. Shope, “Radiation-induced skin injuries from fluoroscopy,” Radiology 197(P), 209 (1995).

R. Aufrichtig, D. L. Wilson, “X-ray fluoroscopy spatio-temporal filtering with object detection,” IEEE Trans. Med. Imaging 14, 733–746 (1995).
[CrossRef] [PubMed]

J. C. Brailean, R. P. Kleihorst, S. N. Efstratiadis, A. K. Katsaggelos, R. L. Lagendijk, “Noise reduction filters for dynamic image sequences: a review,” Proc. IEEE 83, 1272–1292 (1995).
[CrossRef]

1994 (4)

A. E. Burgess, “Statistically defined backgrounds: performance of a modified nonprewhitening observer model,” J. Opt. Soc. Am. A 11, 1237–1242 (1994).
[CrossRef]

R. Aufrichtig, P. Xue, C. W. Thomas, G. C. Gilmore, D. L. Wilson, “Perceptual comparison of pulsed and continuous fluoroscopy,” Med. Phys. 21, 245–256 (1994).
[CrossRef] [PubMed]

R. Aufrichtig, C. Thomas, P. Xue, D. L. Wilson, “Model for perception of pulsed fluoroscopy image sequences,” J. Opt. Soc. Am. A 11, 3167–3176 (1994).
[CrossRef]

D. L. Wilson, P. Xue, R. Aufrichtig, “Perception of fluoroscopy last-image-hold,” Med. Phys. 21, 1875–1883 (1994).
[CrossRef] [PubMed]

1993 (1)

C. L. Chan, A. K. Katsaggelos, A. V. Sahakian, “Image sequences filtering in quantum-limited noise with applications to low-dose fluoroscopy,” IEEE Trans. Med. Imaging 12, 610–621 (1993).
[CrossRef]

1992 (2)

J. P. Rolland, H. H. Barrett, “Effect of random background inhomogeneity on observer detection performance,” J. Opt. Soc. Am. A 9, 649–658 (1992).
[CrossRef] [PubMed]

L. Alvarez, P.-L. Lions, J.-M. Morel, “Image selective smoothing and edge detection by nonlinear diffusion. II,” SIAM J. Numer. Anal. 29, 845–866 (1992).
[CrossRef]

1991 (1)

G. R. Arce, “Multistage order statistic filters for image sequence processing,” IEEE Trans. Signal Process. 39, 1146–1163 (1991).
[CrossRef]

1987 (1)

1985 (2)

K. J. Myers, H. H. Barrett, M. C. Borgstrom, D. D. Patton, G. W. Seeley, “Effect of noise correlation on detectability of disk signals in medical imaging,” J. Opt. Soc. Am. A 2, 1752–1759 (1985).
[CrossRef] [PubMed]

D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern. Anal. Mach. Intell. PAMI-7, 165–177 (1985).
[CrossRef]

1984 (1)

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

1979 (1)

1972 (1)

1970 (1)

Abbey, C. K.

Ahumada, A. J.

Alvarez, L.

L. Alvarez, P.-L. Lions, J.-M. Morel, “Image selective smoothing and edge detection by nonlinear diffusion. II,” SIAM J. Numer. Anal. 29, 845–866 (1992).
[CrossRef]

Arce, G. R.

G. R. Arce, “Multistage order statistic filters for image sequence processing,” IEEE Trans. Signal Process. 39, 1146–1163 (1991).
[CrossRef]

Astola, J.

J. Astola, P. Kuosmanen, Fundamentals of Nonlinear Digital Filtering (CRC Press, Boca Raton, Fla., 1997).

Aufrichtig, R.

D. L. Wilson, K. N. Jabri, P. Xue, R. Aufrichtig, “Perceived noise versus display noise in temporally filtered image sequences,” J. Electron. Imaging 5, 490–495 (1996).
[CrossRef]

R. Aufrichtig, D. L. Wilson, “X-ray fluoroscopy spatio-temporal filtering with object detection,” IEEE Trans. Med. Imaging 14, 733–746 (1995).
[CrossRef] [PubMed]

R. Aufrichtig, P. Xue, C. W. Thomas, G. C. Gilmore, D. L. Wilson, “Perceptual comparison of pulsed and continuous fluoroscopy,” Med. Phys. 21, 245–256 (1994).
[CrossRef] [PubMed]

D. L. Wilson, P. Xue, R. Aufrichtig, “Perception of fluoroscopy last-image-hold,” Med. Phys. 21, 1875–1883 (1994).
[CrossRef] [PubMed]

R. Aufrichtig, C. Thomas, P. Xue, D. L. Wilson, “Model for perception of pulsed fluoroscopy image sequences,” J. Opt. Soc. Am. A 11, 3167–3176 (1994).
[CrossRef]

Barrett, H. H.

Blackwell, K. T.

K. T. Blackwell, “The effect of white and filtered noise on contrast detection thresholds,” Vision Res. 38, 267–280 (1998).
[CrossRef] [PubMed]

Blake, R.

R. Sekular, R. Blake, Perception (McGraw-Hill, New York, 1990).

Borgstrom, M. C.

Brailean, J. C.

J. C. Brailean, R. P. Kleihorst, S. N. Efstratiadis, A. K. Katsaggelos, R. L. Lagendijk, “Noise reduction filters for dynamic image sequences: a review,” Proc. IEEE 83, 1272–1292 (1995).
[CrossRef]

Burger, P.

G. Z. Yang, P. Burger, D. N. Firmin, S. R. Underwood, “Structure adaptive anisotropic image filtering,” Image Vision Comput. 14, 135–145 (1996).
[CrossRef]

Burgess, A. E.

Chan, C. L.

C. L. Chan, A. K. Katsaggelos, A. V. Sahakian, “Image sequences filtering in quantum-limited noise with applications to low-dose fluoroscopy,” IEEE Trans. Med. Imaging 12, 610–621 (1993).
[CrossRef]

Chavel, P.

D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern. Anal. Mach. Intell. PAMI-7, 165–177 (1985).
[CrossRef]

Doi, K.

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

Eckstein, M. P.

M. P. Eckstein, A. J. Ahumada, A. B. Watson, “Visual signal detection in structured backgrounds. II. Effects of contrast gain control, background variations, and white noise,” J. Opt. Soc. Am. A 14, 2406–2419 (1997).
[CrossRef]

J. S. Whiting, M. P. Eckstein, C. A. Morioka, N. L. Eigler, “Effect of additive noise, signal contrast, and feature motion on visual detection in structured noise,” in Medical Imaging 1996: Image Perception, H. L. Kundel, ed., Proc. SPIE2712, 26–38 (1996).
[CrossRef]

Efstratiadis, S. N.

J. C. Brailean, R. P. Kleihorst, S. N. Efstratiadis, A. K. Katsaggelos, R. L. Lagendijk, “Noise reduction filters for dynamic image sequences: a review,” Proc. IEEE 83, 1272–1292 (1995).
[CrossRef]

Eigler, N. L.

J. S. Whiting, M. P. Eckstein, C. A. Morioka, N. L. Eigler, “Effect of additive noise, signal contrast, and feature motion on visual detection in structured noise,” in Medical Imaging 1996: Image Perception, H. L. Kundel, ed., Proc. SPIE2712, 26–38 (1996).
[CrossRef]

Fiete, R. D.

Firmin, D. N.

G. Z. Yang, P. Burger, D. N. Firmin, S. R. Underwood, “Structure adaptive anisotropic image filtering,” Image Vision Comput. 14, 135–145 (1996).
[CrossRef]

Gilmore, G. C.

P. Xue, C. W. Thomas, G. C. Gilmore, D. L. Wilson, “An adaptive reference/test paradigm: application to pulsed fluoroscopy perception,” Behav. Res. Methods Instrum. Comput. 30, 332–348 (1998).
[CrossRef]

R. Aufrichtig, P. Xue, C. W. Thomas, G. C. Gilmore, D. L. Wilson, “Perceptual comparison of pulsed and continuous fluoroscopy,” Med. Phys. 21, 245–256 (1994).
[CrossRef] [PubMed]

Gonzalez, R. C.

R. C. Gonzalez, R. E. Woods, Digital Image Processing (Addison-Wesley, Reading, Mass., 1992).

Ishida, M.

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

Jabri, K. N.

D. L. Wilson, K. N. Jabri, P. Xue, R. Aufrichtig, “Perceived noise versus display noise in temporally filtered image sequences,” J. Electron. Imaging 5, 490–495 (1996).
[CrossRef]

Jain, A. K.

A. K. Jain, Fundamentals of Digital Image Processing (Prentice-Hall, Englewood Cliffs, N.J., 1989).

Jessell, T. M.

E. R. Kandel, J. H. Schwartz, T. M. Jessell, Principles of Neural Science (Appleton & Lange, Norwalk, Conn., 1991).

Julesz, B.

Kandel, E. R.

E. R. Kandel, J. H. Schwartz, T. M. Jessell, Principles of Neural Science (Appleton & Lange, Norwalk, Conn., 1991).

Katsaggelos, A. K.

J. C. Brailean, R. P. Kleihorst, S. N. Efstratiadis, A. K. Katsaggelos, R. L. Lagendijk, “Noise reduction filters for dynamic image sequences: a review,” Proc. IEEE 83, 1272–1292 (1995).
[CrossRef]

C. L. Chan, A. K. Katsaggelos, A. V. Sahakian, “Image sequences filtering in quantum-limited noise with applications to low-dose fluoroscopy,” IEEE Trans. Med. Imaging 12, 610–621 (1993).
[CrossRef]

Kelly, D. H.

Kleihorst, R. P.

J. C. Brailean, R. P. Kleihorst, S. N. Efstratiadis, A. K. Katsaggelos, R. L. Lagendijk, “Noise reduction filters for dynamic image sequences: a review,” Proc. IEEE 83, 1272–1292 (1995).
[CrossRef]

Kuan, D. T.

D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern. Anal. Mach. Intell. PAMI-7, 165–177 (1985).
[CrossRef]

Kunt, M.

C. van den Branden Lambrecht, M. Kunt, “Characterization of human visual sensitivity for video imaging applications,” Signal Process. 67, 255–269 (1998).
[CrossRef]

Kuosmanen, P.

J. Astola, P. Kuosmanen, Fundamentals of Nonlinear Digital Filtering (CRC Press, Boca Raton, Fla., 1997).

Lagendijk, R. L.

J. C. Brailean, R. P. Kleihorst, S. N. Efstratiadis, A. K. Katsaggelos, R. L. Lagendijk, “Noise reduction filters for dynamic image sequences: a review,” Proc. IEEE 83, 1272–1292 (1995).
[CrossRef]

Lai, A. H. S.

N. H. C. Yung, A. H. S. Lai, “Performance evaluation of a feature-preserving filtering algorithm for removing additive random noise in digital images,” Opt. Eng. 35, 1871–1885 (1996).
[CrossRef]

Lehr, J. L.

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

Li, X.

Lions, P.-L.

L. Alvarez, P.-L. Lions, J.-M. Morel, “Image selective smoothing and edge detection by nonlinear diffusion. II,” SIAM J. Numer. Anal. 29, 845–866 (1992).
[CrossRef]

Loo, L.-N.

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

Manjeshwar, R. M.

Matsui, T.

T. Matsui, “Theoretical analysis of perceptual responses to flashed sinusoidal waves using a multichannel spatiotemporal human vision model,” Electron. Commun. Jpn. Pt. 3 81, 51–62 (1998).
[CrossRef]

McDonough, R. N.

R. N. McDonough, A. D. Whalen, Detection of Signals in Noise, 2nd ed. (Academic, San Diego, Calif., 1995).

Metz, C. E.

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

Morel, J.-M.

L. Alvarez, P.-L. Lions, J.-M. Morel, “Image selective smoothing and edge detection by nonlinear diffusion. II,” SIAM J. Numer. Anal. 29, 845–866 (1992).
[CrossRef]

Morioka, C. A.

J. S. Whiting, M. P. Eckstein, C. A. Morioka, N. L. Eigler, “Effect of additive noise, signal contrast, and feature motion on visual detection in structured noise,” in Medical Imaging 1996: Image Perception, H. L. Kundel, ed., Proc. SPIE2712, 26–38 (1996).
[CrossRef]

Myers, K. J.

Patton, D. D.

Peebles, P. Z.

P. Z. Peebles, Communication System Principles (Addison-Wesley, Reading, Mass., 1976).

Pollehn, H.

Pratt, W. K.

W. K. Pratt, Digital Image Processing, 2nd ed. (Wiley, New York, 1991).

Roehrig, H.

Rolland, J. P.

Sahakian, A. V.

C. L. Chan, A. K. Katsaggelos, A. V. Sahakian, “Image sequences filtering in quantum-limited noise with applications to low-dose fluoroscopy,” IEEE Trans. Med. Imaging 12, 610–621 (1993).
[CrossRef]

Sawchuk, A. A.

D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern. Anal. Mach. Intell. PAMI-7, 165–177 (1985).
[CrossRef]

Schwartz, J. H.

E. R. Kandel, J. H. Schwartz, T. M. Jessell, Principles of Neural Science (Appleton & Lange, Norwalk, Conn., 1991).

Seeley, G. W.

Sekular, R.

R. Sekular, R. Blake, Perception (McGraw-Hill, New York, 1990).

Shope, T. B.

T. B. Shope, “Radiation-induced skin injuries from fluoroscopy,” Radiology 197(P), 209 (1995).

Smith, W. E.

Strand, T. C.

D. T. Kuan, A. A. Sawchuk, T. C. Strand, P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern. Anal. Mach. Intell. PAMI-7, 165–177 (1985).
[CrossRef]

Stromeyer, C. F.

Thomas, C.

Thomas, C. W.

P. Xue, C. W. Thomas, G. C. Gilmore, D. L. Wilson, “An adaptive reference/test paradigm: application to pulsed fluoroscopy perception,” Behav. Res. Methods Instrum. Comput. 30, 332–348 (1998).
[CrossRef]

R. Aufrichtig, P. Xue, C. W. Thomas, G. C. Gilmore, D. L. Wilson, “Perceptual comparison of pulsed and continuous fluoroscopy,” Med. Phys. 21, 245–256 (1994).
[CrossRef] [PubMed]

Underwood, S. R.

G. Z. Yang, P. Burger, D. N. Firmin, S. R. Underwood, “Structure adaptive anisotropic image filtering,” Image Vision Comput. 14, 135–145 (1996).
[CrossRef]

van den Branden Lambrecht, C.

C. van den Branden Lambrecht, M. Kunt, “Characterization of human visual sensitivity for video imaging applications,” Signal Process. 67, 255–269 (1998).
[CrossRef]

Watson, A. B.

Whalen, A. D.

R. N. McDonough, A. D. Whalen, Detection of Signals in Noise, 2nd ed. (Academic, San Diego, Calif., 1995).

Whiting, J. S.

J. S. Whiting, M. P. Eckstein, C. A. Morioka, N. L. Eigler, “Effect of additive noise, signal contrast, and feature motion on visual detection in structured noise,” in Medical Imaging 1996: Image Perception, H. L. Kundel, ed., Proc. SPIE2712, 26–38 (1996).
[CrossRef]

Wilson, D. L.

D. L. Wilson, R. M. Manjeshwar, “Role of phase information and eye pursuit in the detection of moving objects in noise,” J. Opt. Soc. Am. A 16, 669–678 (1999).
[CrossRef]

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Figures (5)

Fig. 1
Fig. 1

Frequency response of the center-weighted averaging filter. Curves F1, F2, and F3 correspond to different filtering levels with noise pixel variance ratios (after filtering, σF2, over before filtering, σNF2) of 0.75, 0.50, and 0.25, respectively. The two-dimensional transfer functions are almost circularly symmetrical, and the responses are shown along the major axes of the two-dimensional frequency space. Spatial frequency is in terms of cycles per degree of visual angle.

Fig. 2
Fig. 2

Sample frame from the nine-alternative forced-choice display. A stationary vertical cylinder is randomly placed in the center of one of nine fields. In experiments either a single frame is displayed or 64 frames are displayed in a repeating loop at 33 frames/s. Viewing time is unlimited, and observers indicate where they think the cylinder resides by clicking a mouse button over the appropriate location. Noise in this figure is unfiltered, and contrast is increased for clarity.

Fig. 3
Fig. 3

Signal contrast sensitivity as a function of spatial filtering level for (a) single image and (b) image sequence experiments. A variance ratio of 1.0 indicates no filtering, and filtering is increased as one goes from left to right along the x axis. Measurements from three observers (AC, CR, and KJ) as well as an average across observers are shown. Each sensitivity value was estimated over 200 trials, resulting in an estimated coefficient of variation of ≈5%. Standard errors of averages across observers are derived by error propagation formulas. For image sequences, a paired t-test that does not take into account the standard error estimates of each measurement shows a significant effect of filtering at all levels as compared with no filtering (p<0.05). The effect of filtering on single images is not significant (p>0.05).

Fig. 4
Fig. 4

Ratio of signal contrast sensitivity in an image sequence to that in a single image is plotted as a function of spatial filtering level. Standard errors of ratios are derived by an error propagation formula. As compared with no filtering, a paired t-test shows a significant effect of filtering (p<0.05) at all the levels.

Fig. 5
Fig. 5

Predictions from two observer models and from pixel noise variance reduction are compared with sensitivity improvement as a function of spatial filtering level for (a) single images and (b) image sequences. Contrast sensitivity for the observer models is the inverse of the signal contrast needed to achieve a SNR [Eq. (2) or (4)] equal to d80%. The result is then scaled to fit the human observer data for the unfiltered case. To compare results with predictions from pixel variance, we scale sensitivity in the unfiltered case by the square root of the variance ratio [1/CF=(1/CNF)/σF2/σNF2], where F and NF refer to filtering and no filtering, respectively.

Equations (7)

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SNRPWMF2= |S(u, v, f)|2N(u, v, f)dudvdf,
SNRPWMF2=|St(f)|2df |Ss(u, v)|2|H(u, v)|2N0dudv,
SNRNPW-HVS2=|S(u, v, f)|2|V(u, v, f)|2dudvdf2|S(u, v, f)|2|V(u, v, f)|4N(u, v, f)dudvdf+Ni(u, v, f)dudvdf,
SNRNPW-HVS2=A |Ss(u, v)|2|Vs(u, v)|2dudv2|Ss(u, v)|2|Vs(u, v)|4|H(u, v)|2N0dudv,
A=|St(f)|2|Vt(f)|2df2|St(f)|2|Vt(f)|4df.
y(m, n)=i=-1+1j=-1+1aijx(m+i, n+j)K+8, aij=Kifi=j=01otherwise.
C=gb-gc,

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