A robust similarity measure for volumetric image registration with outliers

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

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