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Article overview
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Cramer-Rao Lower Bound for Point Based Image Registration with Heteroscedastic Error Model for Application in Single Molecule Microscopy | | E.A.K. Cohen
; D. Kim
; R.J. Ober
; | | Date: |
22 Apr 2015 | | Abstract: | The Cramer-Rao lower bound for the estimation of the affine transformation
parameters in a multivariate heteroscedastic errors-in-variables model is
derived. The model is suitable for feature-based image registration in which
both sets of control points are localized with errors whose covariance matrices
vary from point to point. With focus given to the registration of fluorescence
microscopy images, the Cramer-Rao lower bound for the estimation of a feature’s
position (e.g. of a single molecule) in a registered image is also derived. In
the particular case where all covariance matrices for the localization errors
are scalar multiples of a common positive definite matrix (e.g. the identity
matrix), as can be assumed in fluorescence microscopy, then simplified
expressions for the Cramer-Rao lower bound are given and under certain
simplifying assumptions these expressions are shown to match asymptotic
distributions for a previously presented set of estimators. Theoretical results
are verified with simulations and experimental data. | | Source: | arXiv, 1504.5781 | | Services: | Forum | Review | PDF | Favorites | |
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