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Image registration error analysis with applications in single molecule microscopy

Image registration error analysis with applications in single molecule microscopy
Image registration error analysis with applications in single molecule microscopy

This paper is concerned with assessing localization errors emanating from the image registration of two monochromatic fluorescence microscopy images. Assuming an affine transform exists between images, registration in this setting typically involves using control points to solve a multivariate linear regression problem; however with measurement errors existing in both sets of variables the use of linear least squares is inappropriate. It is shown that image registration is an errors-in-variable problem and as such the correct method is to use generalized least squares. Traditionally this requires the measurement errors to be independent and identically distributed (iid); an assumption that is rarely satisfied in practical situations. An extension of the multivariate generalized least squares estimator that allows non-iid noise is applied. The distributional properties of the estimators are used to derive localization errors emanating from the image registration process in terms of photon counts and experimental parameters.

Image registration, Microscopy, Total least squares methods
996-999
Institute of Electrical and Electronics Engineers Inc.
Cohen, E. A.K.
97c75d1e-6c18-4e03-8e7a-29472b32d9c2
Ober, R. J.
31f4d47f-fb49-44f5-8ff6-87fc4aff3d36
Cohen, E. A.K.
97c75d1e-6c18-4e03-8e7a-29472b32d9c2
Ober, R. J.
31f4d47f-fb49-44f5-8ff6-87fc4aff3d36

Cohen, E. A.K. and Ober, R. J. (2012) Image registration error analysis with applications in single molecule microscopy. In 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Proceedings. Institute of Electrical and Electronics Engineers Inc. pp. 996-999 . (doi:10.1109/ISBI.2012.6235725).

Record type: Conference or Workshop Item (Paper)

Abstract

This paper is concerned with assessing localization errors emanating from the image registration of two monochromatic fluorescence microscopy images. Assuming an affine transform exists between images, registration in this setting typically involves using control points to solve a multivariate linear regression problem; however with measurement errors existing in both sets of variables the use of linear least squares is inappropriate. It is shown that image registration is an errors-in-variable problem and as such the correct method is to use generalized least squares. Traditionally this requires the measurement errors to be independent and identically distributed (iid); an assumption that is rarely satisfied in practical situations. An extension of the multivariate generalized least squares estimator that allows non-iid noise is applied. The distributional properties of the estimators are used to derive localization errors emanating from the image registration process in terms of photon counts and experimental parameters.

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More information

Published date: 2012
Venue - Dates: 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macr, Barcelona, Spain, 2012-05-02 - 2012-05-05
Keywords: Image registration, Microscopy, Total least squares methods

Identifiers

Local EPrints ID: 423629
URI: https://eprints.soton.ac.uk/id/eprint/423629
PURE UUID: e7ac8e61-3497-475c-a565-9bba865c356f
ORCID for R. J. Ober: ORCID iD orcid.org/0000-0002-1290-7430

Catalogue record

Date deposited: 27 Sep 2018 16:30
Last modified: 20 Jul 2019 00:22

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Contributors

Author: E. A.K. Cohen
Author: R. J. Ober ORCID iD

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