Similarity measures for non-rigid registration

Non-rigid multimodal registration requires similarity measure with two important properties: locality and multimodality. 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.