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Poster communications

Registration and error estimation in correlated multimodal imaging

Abstract : Image registration methods are used in a wide range of applications, in particular in correlated multimodal imaging in life science, yet we often lack an estimate of the associated registration error. In this work we aim to provide such estimates as a quality metric for image registration. Our method relies on multivariate multiple linear regression analysis which provides both image registration itself and registration error estimates. Since linear regression is flexible, models can be extended to integrate constraints such as rigid transformations. This is also known as the orthogonal Procrustes problem. We present the different methods for error estimation used in the correlated multi-modal imaging field, but also the ones used in the registration literature. Finally we provide an implementation of our registration framework as a plugin under Icy software.
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Contributor : Perrine Paul-Gilloteaux <>
Submitted on : Wednesday, July 29, 2020 - 2:12:06 PM
Last modification on : Thursday, July 30, 2020 - 3:15:07 PM


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  • HAL Id : hal-02744368, version 1



Guillaume Potier, Stephan Kunne, Frédéric Lavancier, Perrine Paul-Gilloteaux. Registration and error estimation in correlated multimodal imaging. Correlated Multimodal Imaging Conference, Nov 2019, Vienna, Austria. ⟨hal-02744368⟩



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