A quality of image match is usually estimated by measuring image similarity. Unfortunately, similarity measures assess only such transformations that change appearance of the deformed image, and in the case of non-rigid registration the results of the
similarity measurement depend on the registration direction. This asymmetric relation leads to registration inconsistency and reduces the quality of registration. In this work we propose a
symmetric registration approach, which improves the registration by measuring similarity in both registration directions. The solution presented in this paper is based on the interaction of both images involved in the registration process. Images interact
with forces, which are according to the Newton's action-reaction law forming a symmetric relationship. These forces may transform both of the images, although in our implementation one of the
images remains fixed. The experiments performed to demonstrate the advantages of the symmetric registration approach involve registration of simple objects, recovering synthetic deformation,
and interpatient registration of real images of head. The results show improvements of registration consistency and also indicate the improvement of registration correctness.
We describe the evaluation of a non-rigid image registration method for multi-modal data. The evaluation is made difficult by the absence of gold standard test data, for which the true transformation from one image to another is known. Different approaches have been used to deal with this deficiency, e.g., by using synthetically warped data, by comparison of anatomic regions of interest identified either manually or automatically, and by direct comparison of the registered data. Each of these approaches are limited and in this paper, we illustrate some of the problems that arise based on their application to the evaluation of our multi-modal non-rigid registration method.
Non-rigid multimodal registration requires similarity measure with two important properties: locality and multi- modality. Unfortunately all commonly used multimodal similarity measures are inherently global and cannot be directly used to estimate local image properties. We have derived a local similarity measure based on joint entropy, which can operate on extremely small image regions, e.g. individual voxels. Using such small image regions reflects in higher sensitivity to noise and partial volume voxels, consequently reducing registration speed and accuracy. To cope with these problems we enhance the similarity measure with image segmentation. Image registration and image segmentation are related tasks, as segmentation can be performed by registering an image to a pre-segmented reference image, while on the other hand registration yields better results when the images are pre-segmented. Because of these interdependences it was anticipated that simultaneous application of registration and segmentation should improve registration as well as segmentation results. Several experiments based on synthetic images were performed to test this assumption. The results obtained show that our method can improve the registration accuracy and reduce the required number of registration steps.