Penalized Maximum-Likelihood Image Reconstruction Using Space-Alternating Generalized EM Algorithms
dc.contributor.author | Fessler, Jeffrey A. | en_US |
dc.contributor.author | Hero, Alfred 0. III | en_US |
dc.date.accessioned | 2011-08-18T18:20:53Z | |
dc.date.available | 2011-08-18T18:20:53Z | |
dc.date.issued | 1995-10 | en_US |
dc.identifier.citation | Fessler, J.A.; Hero, A.O. (1995). "IIIPenalized Maximum-Likelihood Image Reconstruction Using Space-Alternating Generalized EM Algorithms." IEEE Transactions on Image Processing 4(10): 1417-1429. <http://hdl.handle.net/2027.42/85850> | en_US |
dc.identifier.issn | 1057-7149 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/85850 | |
dc.description.abstract | Most expectation-maximization (EM) type algorithms for penalized maximum-likelihood image reconstruction converge slowly, particularly when one incorporates additive background effects such as scatter, random coincidences, dark current, or cosmic radiation. In addition, regularizing smoothness penalties (or priors) introduce parameter coupling, rendering intractable the M-steps of most EM-type algorithms. This paper presents space-alternating generalized EM (SAGE) algorithms for image reconstruction, which update the parameters sequentially using a sequence of small “hidden” data spaces, rather than simultaneously using one large complete-data space. The sequential update decouples the M-step, so the maximization can typically be performed analytically. We introduce new hidden-data spaces that are less informative than the conventional complete-data space for Poisson data and that yield significant improvements in convergence rate. This acceleration is due to statistical considerations, not numerical overrelaxation methods, so monotonic increases in the objective function are guaranteed. We provide a general global convergence proof for SAGE methods with nonnegativity constraints. | en_US |
dc.publisher | IEEE | en_US |
dc.title | Penalized Maximum-Likelihood Image Reconstruction Using Space-Alternating Generalized EM Algorithms | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Biomedical Engineering | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Electrical Engineering and Computer Science. | en_US |
dc.identifier.pmid | 18291973 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/85850/1/Fessler102.pdf | |
dc.identifier.doi | 10.1109/83.465106 | en_US |
dc.identifier.source | IEEE Transactions on Image Processing | en_US |
dc.owningcollname | Electrical Engineering and Computer Science, Department of (EECS) |
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