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Optics Express

Optics Express

  • Editor: J. H. Eberly
  • Vol. 4, Iss. 6 — Mar. 15, 1999
  • pp: 193–199
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Feature stabilized digital x-ray coronary angiograms improve human visual detection in JPEG compressed images

M. P. Eckstein, J. L. Bartroff, C. A. Morioka, D. J. Vodopich, and J. S. Whiting  »View Author Affiliations


Optics Express, Vol. 4, Issue 6, pp. 193-199 (1999)
http://dx.doi.org/10.1364/OE.4.000193


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Abstract

We evaluate the effect on human visual detection of a display method that stabilizes the motion of the feature of interest in sequential JPEG compressed x-ray coronary angiogram images. At all levels of image compression the feature stabilized display significantly improved performance with respect to the standard display where the artery is moving. In addition, for both the moving artery and stabilized display, human performance with images compressed at 15:1 was not significantly different from performance with the uncompressed images.

© Optical Society of America

1. Introduction

Cardiac catherization laboratories are quickly moving towards digital angiographic technology that allows immediate review, quantitative analysis and image processing of the coronary angiograms [1

1. S. E. Nissen et al., “Cardiac Aniography Without Cine Film: Erecting a Tower of Babel in the Cardiac Catheterization Laboratory,” J. Am. College Cardiology 24, 834–837 (1994). [CrossRef]

]. Although there has been great progress in computer quantitative analysis of x-ray coronary angiograms, visual assessment of the disease by the physician still remains the most important diagnostic procedure. In this context, the digital format readily allows for any image processing and/or manipulation of the display methods that could improve the physician's visual detection, classification and/or estimation of a lesion.

For example, psychophysical studies have investigated visual detection of signals embedded in spatially and temporally varying white noise. In this case, detection performance for an observer that temporally integrates all the images in the sequence prior to detection will increase as the number of frames in the sequence increases [2

2. J. S. Whiting, D. Honig, D. Carterette, and N. L. Eigler, “Observer performance in dynamic displays: effect of frame rate on visual signal detection in noisy images,” Proc. SPIE 1453, 165–167 (1991). [CrossRef]

]. However, human observers are not able to unlimitedly and optimally integrate through time all images in a sequence [2–6

2. J. S. Whiting, D. Honig, D. Carterette, and N. L. Eigler, “Observer performance in dynamic displays: effect of frame rate on visual signal detection in noisy images,” Proc. SPIE 1453, 165–167 (1991). [CrossRef]

]. Instead, humans have a limited temporal window of integration allowing them to integrate up to 700-1000 ms. of data [2

2. J. S. Whiting, D. Honig, D. Carterette, and N. L. Eigler, “Observer performance in dynamic displays: effect of frame rate on visual signal detection in noisy images,” Proc. SPIE 1453, 165–167 (1991). [CrossRef]

,3

3. M. P. Eckstein, J. S. Whiting, and J. P. Thomas, “Detection and discrimination of moving signals in Gaussian uncorrelated noise,” Proc. SPIE 2712, 9–25 (1996). [CrossRef]

] and with decreasing integration efficiency as the number of frames increases [2–4

2. J. S. Whiting, D. Honig, D. Carterette, and N. L. Eigler, “Observer performance in dynamic displays: effect of frame rate on visual signal detection in noisy images,” Proc. SPIE 1453, 165–167 (1991). [CrossRef]

]. Therefore, when noisy image sequences are displayed, increasing the display frame rate increases the number of frames displayed within the human integrating window, allowing for more noisy frames to be more efficiently integrated and therefore improve human detection performance [2

2. J. S. Whiting, D. Honig, D. Carterette, and N. L. Eigler, “Observer performance in dynamic displays: effect of frame rate on visual signal detection in noisy images,” Proc. SPIE 1453, 165–167 (1991). [CrossRef]

].

A potential problem when applying this concept to real noisy dynamic medical images such as x-ray coronary angiograms is that the feature of interest (whether it is the artery itself or a morphological feature within an arterial segment) is moving. As a result, as the display frame rate is increased the motion becomes more rapid and abrupt, making visual tracking by the human observer difficult. The difficulty in visual tracking can have detrimental effects on human performance [7

7. N. L. Eigler, M. P. Eckstein, K. Maher, D. Honig, and J. S. Whiting, “Effect of a stenosis stabilized display on morphological feature detection,” Circ , 89, 2700–2709 (1994). [CrossRef]

]. Eigler et al. [7

7. N. L. Eigler, M. P. Eckstein, K. Maher, D. Honig, and J. S. Whiting, “Effect of a stenosis stabilized display on morphological feature detection,” Circ , 89, 2700–2709 (1994). [CrossRef]

] proposed an optimized display for coronary angiograms where each image of the sequence is digitally shifted so that the feature of interest within an artery remains fixed at the center of the screen while the background moves. This stabilized display (S.D.) permits fast display of the image sequences while allowing the observer to scrutinize the feature of interest without having to eye-track rapid and erratic arterial motion.

Figure 1. Left standard moving artery display (M.A). Right the stabilized display (S.D.) [Media 1] [Media 2] [Media 3] [Media 4]

2. Methods

2.1 Acquisition of x-ray coronary angiograms

The clinical digital coronary angiograms were acquired at 30 frames/s with a 7-in. image intensifier filed size (Advantx/DXC, General Electric Medical Systems). The parameters used in the image acquisition were a standard automatic exposure control at 0.30 μGy per frame and extended-dynamic-range-enabled video circuitry. The images were digitized with a linear analog amplification and lookup table to achieve a 512 × 512 pixel matrix with a resolution of 0.3 mm/pixel and 256 gray levels.

2.2 Generation of simulated arteries and lesions for an individual frame

The algorithm to create the simulated arterial segments and lesions is intended to mimic the image generation process of x-ray coronary angiograms. The present algorithm is an extension of the one previously used [7

7. N. L. Eigler, M. P. Eckstein, K. Maher, D. Honig, and J. S. Whiting, “Effect of a stenosis stabilized display on morphological feature detection,” Circ , 89, 2700–2709 (1994). [CrossRef]

] and now includes scattering and veiling glare. Details about the algorithm to generate the computer simulated arteries and lesion (filling defect) are discussed in the Appendix.

For our test-images the projected simulated arteries are 3-D right circular cylinders with a diameter of 12 pixels (3.6 mm), a sinusoidally modulated narrowing in diameter toward the center (minimum diameter of 8 pixels), and a length of 50 pixels (15.0 mm). Four simulated arteries were generated for each test-image and projected 32 pixels apart (Figure 2). The attenuation coefficient, μ, was set to 0.16/mm to produce simulated arteries with the same projected intensity as real angiograms of coronary arteries of the same diameter.

Figure 2. Test image-sequences consisting of four simulated arterial segments (projected 3-D right cylinders with narrowing towards the center). The subtle lesion (a bright spot) is located at the vertical and horizontal center of the bottom right simulated arterial segment. From left to right: Uncompressed moving artery display, uncompressed stabilized display, 30:1 JPEG compressed moving artery and 30:1 JPEG compressed stabilized display. [Media 5] [Media 6] [Media 7] [Media 8]

The target consisted of a simulated filling defect with a hemi-ellipsoidal shape located at the vertical and horizontal center of one of the four simulated arteries with a diameter of 6 pixels (Figure 2). We simulated imaging system blur caused by the physical extent of the x-ray focal spot and image intensifier unsharpness by convolving the projected cylinders with a isotropic Gaussian point spread function with standard deviation of 1 pixel (0.3 mm).

2.3 Generation of arterial motion and feature stabilization

2.4 JPEG compression of images

Images were compressed and decompressed with the fifth public release of the Independent JPEG Group’s free JPEG software. Since a given quality level results in varying compression ratios for different images, a program was written that would iteratively modify the quality level of the compression until the target compression ratio was achieved (with a tolerance of up to 5 % error). In this way the set of 100 image sequences were compressed to achieve final compression ratios (averaged compression across all image sequences ± standard deviation) of 6.94 ± 0.17, 14.89 ± 0.156 and 29.79 ± 0.295.

2.5 Psychophysical studies

2.6 Data Analysis:

Accuracy for a given observer in a given experimental condition was quantified by computing the percent of trials (Pc) that the observer correctly detected the target. Pc was then transformed to an index of detectability (da) for a 4 alternative forced choice given by [12

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

]:

Pc(da,M)=+g(zda)[G(z)]M1dz
(1)

where , g(z) = 1/√2π exp(-1/2 x 2), G(z) is the cumulative Gaussian, M is the number of alternatives. The index of detectability has been shown to be approximately constant with the number of alternatives in the task and linear with signal contrast for human visual detection in white noise [13

13. A. E. Burgess and H. Ghandeharian, “Visual signal detection. II. Signal-location identification,” J. Opt. Soc. Am. A 1, 906–910 (1984). [CrossRef] [PubMed]

] and x-ray coronary angiograms [14

14. M. P. Eckstein and J. S. Whiting, “Visual signal detection in structured backgrounds I. Effect of number of possible locations and signal contrast,” J. Opt. Soc. Am. A 13, 1777–1787 (1996). [CrossRef]

, 15

15. J. S. Whiting, M. P. Eckstein, C. A. Morioka, and N. L. Eigler, “Effect of additive noise, signal contrast and feature motion on signal detection in structured noise,” Proc. SPIE 2712, 39–47 (1996).

].

Figure 3. Human performance (da) for 4 observers detecting a low-contrast feature as a function of JPEG compression ratio for the standard moving artery display (M.A.) and the stabilized display (S.D.). For each graph the highest and lowest performance points are labeled with the corresponding Pc performance level.

3. Results

Figure 3 shows performance as measured by da for each observer for the M.A. and S.D. as a function of increasing JPEG image compression. Performance for all observers is larger for the S.D. display at all levels of compression. There is a reduction in performance with increasing compression for all observers and both display types. A within subject factorial ANOVA resulted in a significant (F(1,3) = 95.31, p < 0.005) effect of display type (S.D. vs. M.A.) and of image compression (F(3,9) = 97.78, p < 0.005) on human performance.

The display type x compression interaction was not significant. Paired comparisons with Bonferroni corrections for multiple comparisons resulted in a significant difference between human performance in the uncompressed condition and 30:1 JPEG compression for both display types. Comparisons between the uncompressed condition and the 7:1 and 15:1 did not reach significance (p > 0.01).

4. Discussion

4.1 Effect of stabilized display

An assumption in our experimental method is that the locations of the feature of interest can be tracked with no errors, so that the feature is perfectly stabilized. If random errors in the tracking (either by manual tracking by a human operator or a computer algorithm) are present, then the feature of interest will not be perfectly stabilized and instead will have random positional jitter through the sequence. We have previously studied the effect of random Gaussian jitter in the position of the artery, and found that human performance does not degrade for a Gaussian jitter with a standard deviation which is small compared to the feature size but did degrade for larger jitter magnitudes. [16

16. J. S. Whiting, M. P. Eckstein, and N. L Eigler, “Effect of motion on feature detectability in dynamicaly displayed coronary arteries,” Fifth Far West Image Perception Conference, Newport , RI (1993).

]. Future studies should assess how much position jitter is introduced in the stabilized display by computer tracking algorithms that are currently being developed [17

17. R. A. Close and J. S. Whiting, “Decomposition of projection image sequences into moving layers,” in Computer Assisted Radiology and Surgery, Editors, H. U. Lemke, M. W. Vannier, K. Inamura, and A. Farman, Elsevier Science , 143-146 (1998).

].

4.2 Effect of image compression

5. Conclusions

With the advent of digital Cathlabs, the use of feature-stabilized and 15:1 compressed coronary angiograms may allow reduced storage limitations and transmission time while improving human detection of morphological features over conventional uncompressed display methods.

6. Appendix

The imaging process is modeled as consisting of an absorption stage and a scattering stage. The incident x-rays are absorbed by the patient body and with higher attenuation by the contrast filled arteries. The attenuated x-rays are then either transmitted directly to the detector as the primary component of the radiation or are scattered to the detector.

In this model the image I(x,y) is composed of a primary component due to the primary beam, P(x,y) and a secondary due to scattered rays, S(x,y):

I(x,y)=P(x,y)+S(x,y)
(A.1)

P(x,y)=IoeDp(x,y)eDa(x,y)
(A.2)

We wish to insert a simulated object in the scene so that the primary component of the image needs to attenuated by the density of the simulated object (e-Ds(x,y)):

Ps(x,y)=P(x,y)eDs(x,y)
(A.3)

where Ps(x,y) is the primary including the simulated object. The density of the simulated object is given by Ds = μts(x,y) where m is the attenuation coefficient and ts(x,y); is the thickness of the contrast filled simulated object traversed by the x-rays as a function of the image position. To embed a simulated object into a real angiographic image, we first subtract an estimate of the secondary from the image, then attenuate the remaining estimated primary corresponding to the simulated artery, and then add back the secondary component:

Is(x,y)=[I(x,y)S(x,y)]eDs(x,y)+S(x,y)
(A.4)

Love and Kruger [10

10. L. A. Love and R A. Kruger, “Scatter estimation for a digital radiographic system using convolution filtering,” Med. Phys. 14, 178–185, (1987). [CrossRef] [PubMed]

] used imaged phantoms to empirically show that the secondary image component due to scattering and veiling glare can be approximated by the convolution of the image and a double exponential kernel [11

11. Note that in this method the scattering and veiling glare is not corrected for the presence of the new simulated object.

]:

S(x,y)=I(x,y)*αh(x,y)+β
(A.5)

where α= 0.483, β= 7.69. The convolution kernel is given by:

h(x,y)=20eAxxoeByyo
(A.6)

where A = B = 2log(2)/√75

The reduction in the density (Eq. A. 4) of the artery due to the filling defect is given by:

Dt(x,y)=κ2.0r2[(xxo)2+(yyo)2]
(A.7)

where xo and yo are the locations of the center of the filling defect, r is the radius, and κ is a contrast factor.

7. Acknowledgements

The authors would like to thank Cedric Heath and George Ruan for their participation as observers in the study. This research was supported by NIH-HL RO1 53455.

References and links

1.

S. E. Nissen et al., “Cardiac Aniography Without Cine Film: Erecting a Tower of Babel in the Cardiac Catheterization Laboratory,” J. Am. College Cardiology 24, 834–837 (1994). [CrossRef]

2.

J. S. Whiting, D. Honig, D. Carterette, and N. L. Eigler, “Observer performance in dynamic displays: effect of frame rate on visual signal detection in noisy images,” Proc. SPIE 1453, 165–167 (1991). [CrossRef]

3.

M. P. Eckstein, J. S. Whiting, and J. P. Thomas, “Detection and discrimination of moving signals in Gaussian uncorrelated noise,” Proc. SPIE 2712, 9–25 (1996). [CrossRef]

4.

M. Tapiovaara, “Efficiency of low-contrast detail detectability in fluoroscopic imaging,” Med. Phys. 24, 655–64 (1997). [CrossRef] [PubMed]

5.

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

6.

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

7.

N. L. Eigler, M. P. Eckstein, K. Maher, D. Honig, and J. S. Whiting, “Effect of a stenosis stabilized display on morphological feature detection,” Circ , 89, 2700–2709 (1994). [CrossRef]

8.

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]

9.

C. A. Morioka, M. P. Eckstein, J. L. Bartroff, J. Hauseleiter, and J. S. Whiting, “JPEG vs. Wavelet image compression of x-ray coronary angiograms,” Opt. Express (to be submitted).

10.

L. A. Love and R A. Kruger, “Scatter estimation for a digital radiographic system using convolution filtering,” Med. Phys. 14, 178–185, (1987). [CrossRef] [PubMed]

11.

Note that in this method the scattering and veiling glare is not corrected for the presence of the new simulated object.

12.

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

13.

A. E. Burgess and H. Ghandeharian, “Visual signal detection. II. Signal-location identification,” J. Opt. Soc. Am. A 1, 906–910 (1984). [CrossRef] [PubMed]

14.

M. P. Eckstein and J. S. Whiting, “Visual signal detection in structured backgrounds I. Effect of number of possible locations and signal contrast,” J. Opt. Soc. Am. A 13, 1777–1787 (1996). [CrossRef]

15.

J. S. Whiting, M. P. Eckstein, C. A. Morioka, and N. L. Eigler, “Effect of additive noise, signal contrast and feature motion on signal detection in structured noise,” Proc. SPIE 2712, 39–47 (1996).

16.

J. S. Whiting, M. P. Eckstein, and N. L Eigler, “Effect of motion on feature detectability in dynamicaly displayed coronary arteries,” Fifth Far West Image Perception Conference, Newport , RI (1993).

17.

R. A. Close and J. S. Whiting, “Decomposition of projection image sequences into moving layers,” in Computer Assisted Radiology and Surgery, Editors, H. U. Lemke, M. W. Vannier, K. Inamura, and A. Farman, Elsevier Science , 143-146 (1998).

18.

M. P. Eckstein, T. D. Wickens, G. Aharonov, G. Ruan, C. A. Morioka, and J. S. Whiting, “Quantifying the limitations of the use of consensus expert committees in ROC studies,” Proc. SPIE 3340, 128–134 (1998). [CrossRef]

OCIS Codes
(100.2980) Image processing : Image enhancement
(170.7440) Medical optics and biotechnology : X-ray imaging
(330.1880) Vision, color, and visual optics : Detection

ToC Category:
Research Papers

History
Original Manuscript: February 16, 1999
Published: March 15, 1999

Citation
M. Eckstein, J. Bartroff, C. Morioka, D. Vodopich, and J. Whiting, "Feature stabilized digital x-ray coronary angiograms improve human visual detection in JPEG compressed images," Opt. Express 4, 193-199 (1999)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-4-6-193


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References

  1. S. E. Nissen et al., "Cardiac Aniography Without Cine Film: Erecting a Tower of Babel in the Cardiac Catheterization Laboratory," J. Am. College Cardiology 24, 834-837 (1994). [CrossRef]
  2. J. S. Whiting, D. Honig, D. Carterette and N. L. Eigler, "Observer performance in dynamic displays: effect of frame rate on visual signal detection in noisy images," Proc. SPIE 1453, 165-167 (1991). [CrossRef]
  3. M. P. Eckstein, J. S. Whiting and J. P. Thomas, "Detection and discrimination of moving signals in Gaussian uncorrelated noise," Proc. SPIE 2712, 9-25 (1996). [CrossRef]
  4. M. Tapiovaara, "Efficiency of low-contrast detail detectability in fluoroscopic imaging," Med. Phys. 24, 655-64 (1997). [CrossRef] [PubMed]
  5. D. L. Wilson, K. N. Jabri, P. Xue and R. Aufrichtig, "Perceived noise versus display noise in temporally filtered image sequences," J. Electron. Imaging 5, 490-495 (1996). [CrossRef]
  6. R. Aufrichtig, C. W. Thomas CW, P. Xue, and D. L. Wilson, "Model for perception of pulsed fluoroscopy image sequences," J. Opt. Soc. Am. A 11, 3167-3176 (1994). [CrossRef]
  7. N. L. Eigler, M. P. Eckstein, K. Maher, D. Honig and J. S. Whiting, "Effect of a stenosis stabilized display on morphological feature detection," Circ, 89, 2700-2709 (1994). [CrossRef]
  8. 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]
  9. A. Morioka, M. P. Eckstein, J. L. Bartroff, J. Hauseleiter and J. S. Whiting, "JPEG vs. Wavelet image compression of x-ray coronary angiograms," Opt. Express (to be submitted).
  10. L. A. Love and R. A. Kruger, "Scatter estimation for a digital radiographic system using convolution filtering," Med. Phys. 14, 178-185, (1987). [CrossRef] [PubMed]
  11. Note that in this method the scattering and veiling glare is not corrected for the presence of the new simulated object.
  12. D. M. Green, J. A. Swets, Signal Detection Theory and Psychophysics (Wiley, N.Y. 1966).
  13. A. E. Burgess and H. Ghandeharian, "Visual signal detection. II. Signal-location identification," J. Opt. Soc. Am. A 1, 906-910 (1984). [CrossRef] [PubMed]
  14. M. P. Eckstein and J. S. Whiting, "Visual signal detection in structured backgrounds I. Effect of number of possible locations and signal contrast," J. Opt. Soc. Am. A 13, 1777-1787 (1996). [CrossRef]
  15. J. S. Whiting, M. P. Eckstein, C. A. Morioka and N. L. Eigler, "Effect of additive noise, signal contrast and feature motion on signal detection in structured noise," Proc. SPIE 2712, 39-47 (1996).
  16. J. S. Whiting and M.P. Eckstein, N. L. Eigler, "Effect of motion on feature detectability in dynamicaly displayed coronary arteries," Fifth Far West Image Perception Conference, Newport, RI (1993).
  17. R. A. Close and J. S. Whiting, "Decomposition of projection image sequences into moving layers," in Computer Assisted Radiology and Surgery, Editors, H.U. Lemke, M.W. Vannier, K. Inamura & A. Farman, Elsevier Science, 143-146 (1998).
  18. M. P. Eckstein, T. D., Wickens, G. Aharonov, G. Ruan, C. A. Morioka, and J. S. Whiting, "Quantifying the limitations of the use of consensus expert committees in ROC studies," Proc. SPIE 3340, 128-134 (1998). [CrossRef]

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