Intrasubject multimodal groupwise registration with the conditional template entropy

Intrasubject multimodal groupwise registration with the conditional template entropy

Polfliet, Mathias (Vrije Universiteit Brussel; IMEC-Solliance; Erasmus MC)
Klein, S. (Erasmus MC)
Huizinga, W. (Erasmus MC)
Paulides, Margarethus M. (Erasmus MC)
Niessen, W.J. (TU Delft ImPhys/Quantitative Imaging; Erasmus MC)
Vandemeulebroucke, Jef (Vrije Universiteit Brussel; IMEC-Solliance)

2018

Image registration is an important task in medical image analysis. Whereas most methods are designed for the registration of two images (pairwise registration), there is an increasing interest in simultaneously aligning more than two images using groupwise registration. Multimodal registration in a groupwise setting remains difficult, due to the lack of generally applicable similarity metrics. In this work, a novel similarity metric for such groupwise registration problems is proposed. The metric calculates the sum of the conditional entropy between each image in the group and a representative template image constructed iteratively using principal component analysis. The proposed metric is validated in extensive experiments on synthetic and intrasubject clinical image data. These experiments showed equivalent or improved registration accuracy compared to other state-of-the-art (dis)similarity metrics and improved transformation consistency compared to pairwise mutual information.

Conditional entropy
Groupwise image registration
Multimodal
Mutual information
Principal component analysis

http://resolver.tudelft.nl/uuid:b3aac995-b6b9-41c8-bb24-ed163b6f4e12

https://doi.org/10.1016/j.media.2018.02.003

1361-8415

Medical Image Analysis, 46, 15-25

Institutional Repository

journal article

© 2018 Mathias Polfliet, S. Klein, W. Huizinga, Margarethus M. Paulides, W.J. Niessen, Jef Vandemeulebroucke