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Comparison of estimation algorithms in single-molecule localization

Comparison of estimation algorithms in single-molecule localization
Comparison of estimation algorithms in single-molecule localization

Different techniques have been advocated for estimating single molecule locations from microscopy images. The question arises as to which technique produces the most accurate results. Various factors, e.g. the stochastic nature of the photon emission/detection process, extraneous additive noise, pixelation, etc., result in the estimated single molecule location deviating from its true location. Here, we review the results presented by [Abraham et. al, Optics Express, 2009, 23352-23373], where the performance of the maximum likelihood and nonlinear least squares estimators for estimating single molecule locations are compared. Our results show that on average both estimators recover the true single molecule location in all scenarios. Comparing the standard deviations of the estimates, we find that in the absence of noise and modeling inaccuracies, the maximum likelihood estimator is more accurate than the non-linear least squares estimator, and attains the best achievable accuracy for the sets of experimental and imaging conditions tested. In the presence of noise and modeling inaccuracies, the maximum likelihood estimator produces results with consistent accuracy across various model mismatches and misspecifications. At high noise levels, neither estimator has an accuracy advantage over the other. We also present new results regarding the performance of the maximum likelihood estimator with respect to the objective function used to fit data containing both additive Gaussian and Poisson noise. Comparisons were also carried out between two localization accuracy measures derived previously. User-friendly software packages were developed for single molecule location estimation (EstimationTool) and localization accuracy calculations (FandPLimitTool).

Cramer-Rao lower bound, Localization, Single molecule, Tracking
SPIE
Abraham, Anish V.
4f71ee5b-b0b1-4e0e-8f64-5cea9ae8f0e0
Ram, Sripad
559bd560-3817-4e53-8c7a-2f08e4518412
Chao, Jerry
550e20b0-8365-42e3-a6fc-1048eb8c2e47
Ward, E. Sally
b31c0877-8abe-485f-b800-244a9d3cd6cc
Ober, Raimund J.
31f4d47f-fb49-44f5-8ff6-87fc4aff3d36
Abraham, Anish V.
4f71ee5b-b0b1-4e0e-8f64-5cea9ae8f0e0
Ram, Sripad
559bd560-3817-4e53-8c7a-2f08e4518412
Chao, Jerry
550e20b0-8365-42e3-a6fc-1048eb8c2e47
Ward, E. Sally
b31c0877-8abe-485f-b800-244a9d3cd6cc
Ober, Raimund J.
31f4d47f-fb49-44f5-8ff6-87fc4aff3d36

Abraham, Anish V., Ram, Sripad, Chao, Jerry, Ward, E. Sally and Ober, Raimund J. (2010) Comparison of estimation algorithms in single-molecule localization. In Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XVII. vol. 7570, SPIE.. (doi:10.1117/12.842178).

Record type: Conference or Workshop Item (Paper)

Abstract

Different techniques have been advocated for estimating single molecule locations from microscopy images. The question arises as to which technique produces the most accurate results. Various factors, e.g. the stochastic nature of the photon emission/detection process, extraneous additive noise, pixelation, etc., result in the estimated single molecule location deviating from its true location. Here, we review the results presented by [Abraham et. al, Optics Express, 2009, 23352-23373], where the performance of the maximum likelihood and nonlinear least squares estimators for estimating single molecule locations are compared. Our results show that on average both estimators recover the true single molecule location in all scenarios. Comparing the standard deviations of the estimates, we find that in the absence of noise and modeling inaccuracies, the maximum likelihood estimator is more accurate than the non-linear least squares estimator, and attains the best achievable accuracy for the sets of experimental and imaging conditions tested. In the presence of noise and modeling inaccuracies, the maximum likelihood estimator produces results with consistent accuracy across various model mismatches and misspecifications. At high noise levels, neither estimator has an accuracy advantage over the other. We also present new results regarding the performance of the maximum likelihood estimator with respect to the objective function used to fit data containing both additive Gaussian and Poisson noise. Comparisons were also carried out between two localization accuracy measures derived previously. User-friendly software packages were developed for single molecule location estimation (EstimationTool) and localization accuracy calculations (FandPLimitTool).

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More information

Published date: 2010
Venue - Dates: Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XVII, San Francisco, CA, United States, 2010-01-25 - 2010-01-28
Keywords: Cramer-Rao lower bound, Localization, Single molecule, Tracking

Identifiers

Local EPrints ID: 423623
URI: http://eprints.soton.ac.uk/id/eprint/423623
PURE UUID: 20ff9c07-62b7-4127-8d1a-9f9141501ef3
ORCID for E. Sally Ward: ORCID iD orcid.org/0000-0003-3232-7238
ORCID for Raimund J. Ober: ORCID iD orcid.org/0000-0002-1290-7430

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Date deposited: 27 Sep 2018 16:30
Last modified: 05 Nov 2019 01:23

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Contributors

Author: Anish V. Abraham
Author: Sripad Ram
Author: Jerry Chao
Author: E. Sally Ward ORCID iD
Author: Raimund J. Ober ORCID iD

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