A Unified Variational Volume Registration Method Based on Automatically Learned Brain Structures

作者：Lederman Carl^*; Joshi Anand; Dinov Ivo; Van Horn John Darrell; Vese Luminita; Toga Arthur

来源：Journal of Mathematical Imaging and Vision, 2016, 55(2): 179-198.

DOI：10.1007/s10851-015-0604-x

• Image registration
• Active surfaces
• Finite elements

We introduce a new volumetric registration technique that effectively combines active surfaces with the finite element method. The method simultaneously aligns multi-label automatic structural segmentation results, which can be obtained by the application of existing segmentation software, to produce an anatomically accurate 3D registration. This registration is obtained by the minimization of a single energy functional. Just like registering raw images, obtaining a 3D registration this way still requires solving a fundamentally ill-posed problem. We explain through academic examples as well as an MRI dataset with manual anatomical labels, which are hidden from the registration method, how the quality of a registration method can be measured and the advantages our approach offers.

• 出版日期2016-6