Publication Details
Mathias Polfliet



This thesis deals with advances in groupwise image registration. Image registration remains 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 given the increasing availability of medical imaging data, both at the individual and the population level. Groupwise image registration has shown promise in a number of applications dealing with large quantities of data, among others to increase registration accuracy and robustness, to improve the transformation smoothness and to reduce the methodological bias compared to pairwise registrations. However, directly comparing groupwise registrations to conventional repeated pairwise registrations is difficult due to several confounding factors impacting the algorithm. In this thesis, as a first contribution, we rigorously evaluate two registration methodologies in several experiments and investigate the differences in performance. Secondly, we fill a gap in current literature on efficient (dis)similarity measures for multimodal groupwise image registration. These two contributions are distributed over four chapters. In Chapter 3, we investigate several registration approaches for the alignment of CT and MRI acquisitions of the mandible in patients with oral squamous cell carcinoma. A comparison is made between rigid and non-rigid approaches with symmetric and asymmetric transformation strategies. The results suggest improved performance in terms of registration accuracy for a symmetric transformation strategy compared to an asymmetric approach, however, the differences were not statistically significant (p=0.054). For this clinical application, we conclude that a rigid registration method is the recommended approach. In Chapter 4, an investigation is performed on different template images for groupwise registrations based on mutual information. Here, template images are employed as a representative image to compare every image in the group to (in terms of its (dis)similarity). We show that the entropy of the template image can have a counter-intuitive contribution to the global dissimilarity value. Additionally, we show that equivalent performance in terms of registration accuracy can be achieved between groupwise and repeated pairwise approaches. In Chapter 5, a novel similarity measure is introduced for multimodal groupwise registration. The conditional template entropy measures the negated average of the pairwise conditional entropy of each image of the group and a template image, which is constructed based on principal component analysis. We show improved or equivalent performance in terms of accuracy compared to other state-of-the-art (dis)similarity measures for multimodal groupwise registration and repeated pairwise registration. Furthermore, groupwise registration vastly outperform repeated pairwise registration in terms of transitive error, a measure which can be interpreted as a measure for the consistency of the transformations in a groupwise setting. In Chapter 6, to further improve on the efficiency of multimodal groupwise registration, we propose a novel dissimilarity measure which is especially adept at registering large groups of images. The dissimilarity measure is formulated as the second smallest eigenvalue of the generalized eigenvalue problem posed in the description of Laplacian eigenmaps. We show little dependence of the measure in terms of computation time with respect to the number of images in the group, and equivalent or improved performance in terms of registration accuracy compared to state-of-the-art groupwise (dis)similarity measures. To summarize, in this work we evaluate groupwise approaches compared to repeated pairwise approaches and show mostly equivalent performance in terms of registration accuracy and robustness and an improved transitivity for groupwise registration. Furthermore, we recommend to use the proposed dissimilarity measure based on Laplacian eigenmaps for large groups of images given its superior or equivalent registration accuracy compared to other measures but superior scaling in terms of execution time with respect to the number of images in the group.