A Bayesian adaptive basis algorithm for single particle reconstruction.
J Struct Biol
; 179(1): 56-67, 2012 Jul.
Article
em En
| MEDLINE
| ID: mdl-22564910
ABSTRACT
Traditional single particle reconstruction methods use either the Fourier or the delta function basis to represent the particle density map. This paper proposes a more flexible algorithm that adaptively chooses the basis based on the data. Because the basis adapts to the data, the reconstruction resolution and signal-to-noise ratio (SNR) is improved compared to a reconstruction with a fixed basis. Moreover, the algorithm automatically masks the particle, thereby separating it from the background. This eliminates the need for ad hoc filtering or masking in the refinement loop. The algorithm is formulated in a Bayesian maximum-a-posteriori framework and uses an efficient optimization algorithm for the maximization. Evaluations using simulated and actual cryogenic electron microscopy data show resolution and SNR improvements as well as the effective masking of particle from background.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Processamento de Imagem Assistida por Computador
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Teorema de Bayes
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Imageamento Tridimensional
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Simulação de Dinâmica Molecular
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
J Struct Biol
Assunto da revista:
BIOLOGIA MOLECULAR
Ano de publicação:
2012
Tipo de documento:
Article
País de afiliação:
Estados Unidos