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Titolo:
Fast maximum-likelihood image-restoration algorithms for three-dimensionalfluorescence microscopy
Autore:
Markham, J; Conchello, JA;
Indirizzi:
Washington Univ, Sch Med, Edward Mallinckrodt Inst Radiol, St Louis, MO 63110 USA Washington Univ St Louis MO USA 63110 Inst Radiol, St Louis, MO 63110 USA Washington Univ, Sch Med, Dept Anat & Neurobiol, St Louis, MO 63110 USA Washington Univ St Louis MO USA 63110 & Neurobiol, St Louis, MO 63110 USA
Titolo Testata:
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION
fascicolo: 5, volume: 18, anno: 2001,
pagine: 1062 - 1071
SICI:
1084-7529(200105)18:5<1062:FMIAFT>2.0.ZU;2-G
Fonte:
ISI
Lingua:
ENG
Soggetto:
OPTICAL-SECTIONING MICROSCOPY; EXPECTATION-MAXIMIZATION ALGORITHM; CONFOCAL IMAGES; 3 DIMENSIONS; EM ALGORITHM; SUPERRESOLUTION; RECONSTRUCTION; CONVERGENCE; CONSTRAINTS; ACCELERATION;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
Engineering, Computing & Technology
Citazioni:
43
Recensione:
Indirizzi per estratti:
Indirizzo: Conchello, JA Washington Univ, Sch Med, Edward Mallinckrodt Inst Radiol, St Louis, MO 63110 USA Washington Univ St Louis MO USA 63110 t Louis, MO 63110 USA
Citazione:
J. Markham e J.A. Conchello, "Fast maximum-likelihood image-restoration algorithms for three-dimensionalfluorescence microscopy", J OPT SOC A, 18(5), 2001, pp. 1062-1071

Abstract

We have evaluated three constrained, iterative restoration algorithms to find a fast, reliable algorithm for maximum-likelihood estimation of fluorescence microscopic images. Two algorithms used a Gaussian approximation to Poisson statistics, with variances computed assuming Poisson noise far the images. The third method used Csiszar's information-divergence. II-divergence! discrepancy measure. Each method included a nonnegativity constraint anda penalty term for regularization; optimization was performed with a conjugate gradient method. Performance of the methods was analyzed with simulated as well as biological images and the results compared with those obtainedwith the expectation-maximization-maximum-likelihood (EM-ML) algorithm. The I-divergence-based algorithm converged fastest and produced images similar to those restored by EM-ML as measured by several metrics. For a noiseless simulated specimen, the number of iterations required for the EM-X;IL method to reach a given log-likelihood value was approximately the square of the number required for the I-divergence-based method to reach the same value. (C) 2001 Optical Society of America.

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Documento generato il 05/04/20 alle ore 21:55:18