Abstract
In this work we propose a new method to estimate the scale hyperparameter for transmission tomography in Nuclear Medicine image reconstruction problems. Within the Bayesian paradigm, Evidence Analysis and circulant preconditioners are used to obtain the scale hyperparameter. For the prior distribution, we use Generalized Gaussian Markov Random Fields (GGMRF), a nonquadratic function that preserves the edges in the reconstructed image. The experimental results indicate that the proposed method produces satisfactory reconstructions.
This work has been partially supported by “Instituto de Salud Carlos III” project FIS G03/185 and by CICYT project TIC2000-1275.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Belge, M., Kilmer, M.E., Miller, E.L.: Wavelet Domain Image Restoration with Adaptative Edge-Preserving Regularization. IEEE Tr. Im. Proc. 9, 597–608 (2000)
Bouman, C.A., Sauer, K.: A Generalized Gaussian Image Model for Edge- Preserving Map Estimation. IEEE Tr. Im. Proc. 2, 296–310 (1993)
Cho, Z.H., Jones, J.P., Singh, M.: Foundation of medical imaging. John Wiley and Sons, Chichester (1993)
Erdogan, H., Fessler, J.A.: Monotonic Algorithms for Transmission Tomography. IEEE Tr. Me. Im. 18, 801–814 (1999)
Fessler, J.A.: ASPIRE (A Sparse Precomputed Iterative Reconstruction Library), http://www.eecs.umich.edu/~fessler/aspire/index.html
Fessler, J.A., Booth, S.D.: Conjugate-Gradient Preconditioning Methods for Shift Variant PET Image Reconstruction. IEEE Tr. Im. Proc. 8, 688–699 (1999)
Hsiao, I., Rangarajan, A., Gindi, G.: Joint MAP Bayesian Tomographic Reconstruction with a Gamma-mixture Prior. IEEE Tr. Im. Proc. 11, 1466–1477 (2002)
López, A., Molina, R., Katsaggelos, A.K.: Scale Hyperparameter Estimation for GGMRF Prior Models with Application to SPECT Images. In: Proc. of IEEE Int. Conf. on Digital Signal Processing, vol. 2, pp. 521–524 (2002)
López, A., Molina, R., Katsaggelos, A.K., Rodriguez, A., López, J.M., Llamas, J.M.: Parameter Estimation in Bayesian Reconstruction of SPECT Images: An Aid in Nuclear Medicine Diagnosis. Int. J. Imaging Syst. Technol. 14, 21–27 (2004)
Mumcuoglu, E.U., Leahy, R., Cherry, S.R., Zhou, Z.: Fast Gradient-based Methods for Bayesian Reconstruction of Transmission and Emission PET Images. IEEE Tr. Me. Im. 13, 687–701 (1993)
Saquib, S.S.: Edge-preserving Models and Efficient Algorithms for ill-osed Inverse Problems in Image Processing. Ph.D. thesis, University of Purdue (1997)
Yu, D.F., Fessler, J.A.: Edge-preserving Tomographic Reconstruction with Nonlocal Regularization. IEEE Tr. Me. Im. 21, 159–173 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
López, A., Molina, R., Katsaggelos, A.K. (2005). Bayesian Reconstruction for Transmission Tomography with Scale Hyperparameter Estimation. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_56
Download citation
DOI: https://doi.org/10.1007/11492542_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26154-4
Online ISBN: 978-3-540-32238-2
eBook Packages: Computer ScienceComputer Science (R0)