Ensemble registration: combining groupwise registration and segmentation

We investigate incorporating structural information from segmentation into a groupwise registration framework. Previous work by Petrovic et al., using MR brain images, showed that using tissue fractions to help construct an intensity reference image gives better results than just using intensity images alone. In their work, a Gaussian Mixture Model (GMM) was fitted to the 1D intensity histogram, then used to construct tissue fraction images for each example. The mean fraction images were then used to create an artificial intensity reference for the registration. By using only the mean, this discarded much of the structural information. We retain all this information, and augment each intensity image with its set of tissue fraction images (and also intensity gradient images) to form an image ensemble for each example. We then perform groupwise registration using these ensembles of images. This groupwise ensemble registration is applied to the same real-world dataset as used by Petrovic et al. Ground-truth labels enable quantitative evaluation to be performed. It is shown that ensemble registration gives quantitatively better results than the algorithm of Petrovic et al., and that the best results are achieved when more than one of the three types of images (intensity, tissue fraction and gradient) are included as an ensemble.