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
Cohen, E. A.K.
97c75d1e-6c18-4e03-8e7a-29472b32d9c2
Ober, R. J.
31f4d47f-fb49-44f5-8ff6-87fc4aff3d36
2012
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.
IEEE.
.
(doi:10.1109/ISBI.2012.6235725).
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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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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
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Local EPrints ID: 423629
URI: http://eprints.soton.ac.uk/id/eprint/423629
PURE UUID: e7ac8e61-3497-475c-a565-9bba865c356f
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Date deposited: 27 Sep 2018 16:30
Last modified: 16 Mar 2024 04:37
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Author:
E. A.K. Cohen
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