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A robust similarity measure for volumetric image registration with outliers

Snape, Patrick; Pszczolkowski, Stefan; Zafeiriou, Stefanos; Tzimiropoulos, Georgios; Ledig, Christian; Rueckert, Daniel

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Authors

Patrick Snape

Stefanos Zafeiriou

Georgios Tzimiropoulos

Christian Ledig

Daniel Rueckert



Abstract

Image registration under challenging realistic conditions is a very important area of research. In this paper, we focus on algorithms that seek to densely align two volumetric images according to a global similarity measure. Despite intensive research in this area, there is still a need for similarity measures that are robust to outliers common to many different types of images. For example, medical image data is often corrupted by intensity inhomogeneities and may contain outliers in the form of pathologies. In this paper we propose a global similarity measure that is robust to both intensity inhomogeneities and outliers without requiring prior knowledge of the type of outliers. We combine the normalised gradients of images with the cosine function and show that it is theoretically robust against a very general class of outliers. Experimentally, we verify the robustness of our measures within two distinct algorithms. Firstly, we embed our similarity measures within a proof-of-concept extension of the Lucas–Kanade algorithm for volumetric data. Finally, we embed our measures within a popular non-rigid alignment framework based on free-form deformations and show it to be robust against both simulated tumours and intensity inhomogeneities.

Citation

Snape, P., Pszczolkowski, S., Zafeiriou, S., Tzimiropoulos, G., Ledig, C., & Rueckert, D. (2016). A robust similarity measure for volumetric image registration with outliers. Image and Vision Computing, 52, https://doi.org/10.1016/j.imavis.2016.05.006

Journal Article Type Article
Acceptance Date May 5, 2016
Online Publication Date May 29, 2016
Publication Date Aug 1, 2016
Deposit Date Jun 20, 2016
Publicly Available Date Jun 20, 2016
Journal Image and Vision Computing
Print ISSN 0262-8856
Electronic ISSN 1872-8138
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 52
DOI https://doi.org/10.1016/j.imavis.2016.05.006
Keywords Image registration; Lucas–Kanade; Normalised gradient; Free-form deformation
Public URL https://nottingham-repository.worktribe.com/output/975624
Publisher URL http://www.sciencedirect.com/science/article/pii/S0262885616300841
Contract Date Jun 20, 2016

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