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J. Imaging 2017, 3(1), 7; doi:10.3390/jimaging3010007

Estimation Methods of the Point Spread Function Axial Position: A Comparative Computational Study

Laboratorio de Microscopia Aplicada a Estudios Moleculares y Celulares, Facultad de Ingeniería, Universidad Nacional de Entre Ríos, Entre Ríos, CP E3100, Argentina
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Academic Editor: Gonzalo Pajares Martinsanz
Received: 24 August 2016 / Revised: 13 January 2017 / Accepted: 16 January 2017 / Published: 24 January 2017
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Abstract

The precise knowledge of the point spread function is central for any imaging system characterization. In fluorescence microscopy, point spread function (PSF) determination has become a common and obligatory task for each new experimental device, mainly due to its strong dependence on acquisition conditions. During the last decade, algorithms have been developed for the precise calculation of the PSF, which fit model parameters that describe image formation on the microscope to experimental data. In order to contribute to this subject, a comparative study of three parameter estimation methods is reported, namely: I-divergence minimization (MIDIV), maximum likelihood (ML) and non-linear least square (LSQR). They were applied to the estimation of the point source position on the optical axis, using a physical model. Methods’ performance was evaluated under different conditions and noise levels using synthetic images and considering success percentage, iteration number, computation time, accuracy and precision. The main results showed that the axial position estimation requires a high SNR to achieve an acceptable success level and higher still to be close to the estimation error lower bound. ML achieved a higher success percentage at lower SNR compared to MIDIV and LSQR with an intrinsic noise source. Only the ML and MIDIV methods achieved the error lower bound, but only with data belonging to the optical axis and high SNR. Extrinsic noise sources worsened the success percentage, but no difference was found between noise sources for the same method for all methods studied. View Full-Text
Keywords: fluorescence wide-field microscopy; optical sectioning; three-dimensional point spread function; inverse problems; parameter estimation; maximum likelihood; non-linear least square; Csiszár I-divergence; accuracy; precision; Cramer–Rao lower bound fluorescence wide-field microscopy; optical sectioning; three-dimensional point spread function; inverse problems; parameter estimation; maximum likelihood; non-linear least square; Csiszár I-divergence; accuracy; precision; Cramer–Rao lower bound
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Diaz Zamboni, J.E.; Casco, V.H. Estimation Methods of the Point Spread Function Axial Position: A Comparative Computational Study. J. Imaging 2017, 3, 7.

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