Abstract. This paper presents a learning method to select best geometric features for deformable brain registration. Best geometric features are selected for each brain location, a...
A fully learning-based framework has been presented for deformable registration of MR brain images. In this framework, the entire brain is first adaptively partitioned into a numbe...
This paper presents a general learning framework for non-rigid registration of MR brain images. Given a set of training MR brain images, three major types of information are partic...
In this paper we evaluate three conceptually different approaches to mesh generation for deformable Finite Element Method (FEM) registration of Magnetic Resonance (MR) images of b...
Andriy Fedorov, Nikos Chrisochoides, Ron Kikinis, ...
We investigated 7 di erent similarity measures for rigid body registration of serial MR brain scans. To assess their accuracy we used a set of 33 clinical 3D serial MR images, manu...
Mark Holden, Derek L. G. Hill, Erika R. E. Denton,...