Map image reconstruction using wavelet decomposition

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 687 LNCS, Issue , 1993, Pages 354-371

  Wu, Zhenyu ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ITERATIVE METHODS; MEDICAL IMAGING; POSITRON EMISSION TOMOGRAPHY; RADAR IMAGING; WAVELET DECOMPOSITION;

BAYESIAN IMAGE RECONSTRUCTION; CONDITIONAL LIKELIHOOD; GAUSSIAN PROCESSES; ITERATIVE ALGORITHM; POSITRON EMISSION TOMOGRAPHY (PET); RECONSTRUCTION ALGORITHMS; RECONSTRUCTION PROBLEMS; SIMULTANEOUS AUTOREGRESSIVE;

IMAGE RECONSTRUCTION;

EID: 79959578233     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0013799     Document Type: Conference Paper

References (25)

1. G. T. Herman, Image Reconstruction from Projections: The Fundamentals of Computerized Tomography. New York: Academic, 1990.
2. Y. Vardi, L. A. Shepp, and L. Kaufman, “A statistical model for positron emission tomography,” J. Amer. Star. Soc., vol. 80, March 1985.
3. L. Shepp and B. F. Logan, “The Fourier transform of a head section,” IEEE Trans. on Nuclear Science, vol. 21, pp. 21-43, 1979.
4. L. Shepp and Y. Vardi, “Maximum likelihood reconstruction in positron emission tomography,” IEEE Trans. on Medical Imaging, vol. 1, pp. 113-122, 1982.
5. D. L. Snyder et al., “Noise and edge artifacts in maximum-likelihood reconstructions for emission tomography,” IEEE Trans. on Medical Imaging, vol. 6, pp. 228-238, 1987.
6. T. Hebert, R. Leahy, and M. Singh, “Fast MLE for SPECT using an intermediate polar representation and a stopping criterion,” IEEE Trans. on Nuclear Science, vol. 34, pp. 615-619, 1988.
7. E. Veklerov and d. Lacer, “Stopping rule for the MLE algorithm based on statistical hypothesis testing,” IEEE Trans. on Medical Imaging, vol. 6, Dec. 1987.
8. D. Snyder and M. Miller, “The use of sieves to stabilize images produced with the EM algorithm for emission tomography,” IEEE Trans. on Nuclear Science, vol. 32, pp. 3864-3872, 1985.
9. B. W. Silverman et al., “A smoothed EM approach to indirect estimation problems, with particular reference to stereology and emission tomography,” J. Roy. Stat. Soc., Series B, vol. 52, pp. 271-302, 1990.
10. S. Geman and D. McClure, “Bayesian image analysis: An application to single photon emission tomography,” Proceedings of the American Stalistical Association, pp. 12-18, 1985.
11. E. Liang and H. Hart, “Bayesian image processing of data from constrained source distributions-non-valued, uncorrelated and correlated constraints,” Bull. Math. Biol., pp. 51-74, 1987.
12. E. Levitan and G. T. Herman, “A maximum a posleriori probability expectation maximization algorithm for image reconstruction in emission tomography,” IEEE Trans. on Medical Imaging, vol. 6, pp. 185-192, 1987.
13. G. T. Herman and D. Odhner, “Performance evaluation of an iterative image reconstruction algorithm for positron emission tomography,” IEEE Trans. on Medical Imaging, vol. 10, pp. 336-346, 1991.
14. T. Hebert and R. Leahy, “A generalized EM algorithm for 3-D Bayesian reconstruction from Poisson data using Gibbs priors,” IEEE Trans. on Medical Imaging, vol. 8, pp. 184-202, 1989.
15. X. Yan and R. Leahy, “MAP image reconstruction using intensity and line processes for emission tomography data,” in Proc. SPIE/SPSE's Syrup. on Elect. Image Sci. & Tech., Feb. 1991.
16. R. Leahy and X. Yan, “Incorporation of anatomical MR data for improved functional imaging with PET,” in Information Processing in Medical Imaging, edited by A.C.F. Colchester and D.J. Hawkes. Presented at XIIth Int. Conf. on IPMI, Feb. 1991.
17. B. Sahiner and A. Yagle, “On the use of wavelets in inversing the Radon transform,” in Proc. IEEE Int. Conf. on Medical Imaging, Oct. 1992.
18. O. Rioul and M. Vetterli, “Wavelets and signal processing,” IEEE Signal Processing, vol. 8, pp. 14-38, Oct. 1991.
19. S. G. Mallat, “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. on Pattern Anal. Machine Intell., vol. 7, pp. 674-693, July 1989.
20. J. W. Woods and S. D. O'Neil, “Subband coding of images,” IEEE Trans. on Acoust. Speech Signal Processing, vol. 34, pp. 1278-1288, Oct. 1986.
21. G. R. Cross and A. K. Jain, “Markov random field texture models,” IEEE Trans. on Pattern Anal. Machine Intell., vol. 5, pp. 25-39, Jan. 1983.
22. A. DePierro, “A modified expectation maximization algorithm for penalized likelihood estimation in emission tomography,” Technical Report #65/91, Instituto de Matemdtica, Universidade Estadual de Campinas, Brazil, 1991.
23. A. DePierro, “A modified expectation maximization algorithm for penalized likelihood estimation in emission tomography,” submitted to J. Inverse Problems; revised Nov., 1992.
24. D. Luenberger, Linear and Nonlinear Programming. Addison-Wesley, 2nd ed., 1984.
25. G. Herman, R. Lewitt, D. Odhner, and S. Rowland, “SNARK89-A programming system for image reconstruction from projections,” MIPG Tech. Report 160, Dept. Radiology, U. of Pennsylvania, 1989.