In this paper, a novel tree-based registration framework is proposed for achieving fast and accurate registration by providing a more appropriate initial deformation field for the image under registration. Specifically, in the training stage, all training real images and a selected portion of simulated images are organized into a combinative tree with the template as the root, and then each training image is registered to the template with the guidance from the intermediate images on its path to the template. In the testing stage, for a given new image, we first attach it as a child node of its most similar image on the tree, and then use the respective deformation field of this image to initialize the registration. In this way, the residual deformation of the new image to the template can be fast and robustly estimated. In the other case, to register a set of new images, we attach them to the tree one by one by allowing similar test images to help each other during the registration. Importantly, after registration of all new images, a new tree is built which is more capable of representing population distribution and thus allowing for better and faster registration for new future images. This method has been evaluated on the real brain MR image datasets, showing that it can achieve better accuracy within less time than both the statistical model based registration method and the tree-based registration method.
['Hongjun Jia', 'Guorong Wu', 'Qian Wang', 'Minjeong Kim', 'Dinggang Shen']
iTree: Fast and accurate image registration based on the combinative and incremental tree