Bayesian Framework for Image Registration Using Eigenfunctions
1999; Elsevier BV; Linguagem: Inglês
10.1016/b978-012692535-7/50081-1
Autores Tópico(s)Morphological variations and asymmetry
ResumoPreserving geometrical properties and topology during registration is a major thrust of the work that is described in this chapter. It examines the diffeomorphic-continuous, one-to-one, onto, and differentiable transformations. Such properties correspond to topological properties of the transformation such as continuity, differentiability, positive-definiteness of the Jacobian, and others. Transformations that are diffeomorphic maintain topology guaranteeing that connected subregions remain connected, neighborhood relationships between structures are preserved, and surfaces are mapped to surfaces. Preserving topology is important for synthesizing individualized electronic atlases; the knowledgebase of the atlas may be transferred to the target anatomy through the topology preserving transformation providing automatic labeling and segmentation. If the total volume of a nucleus, ventricle, or cortical subregion is an important statistic, it can be generated automatically. Topology-preserving transformations that map the template to the target can also be used to study the physical properties of the target anatomy such as mean shape and variation. The class of diffeomorphic transformations limits the registration of brain images to regions of brain anatomy with equivalent topology. These regions typically include deep subcortical structures such as the thalamus, caudate, ventricles, and even some of the major sulci.
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