Sparse multi-scale diffeomorphic registration: the kernel bundle framework

TY - JOUR

T1 - Sparse multi-scale diffeomorphic registration

T2 - the kernel bundle framework

AU - Sommer, Stefan Horst

AU - Lauze, Francois Bernard

AU - Nielsen, Mads

AU - Pennec, Xavier

PY - 2013/7

Y1 - 2013/7

N2 - In order to detect small-scale deformations during disease propagation while allowing large-scale deformation needed for inter-subject registration, we wish to model deformation at multiple scales and represent the deformation compactly at the relevant scales only. This paper presents the kernel bundle extension of the LDDMM framework that allows multiple kernels at multiple scales to be incorporated in the registration. We combine sparsity priors with the kernel bundle resulting in compact representations across scales, and we present the mathematical foundation of the framework with derivation of the KB-EPDiff evolution equations. Through examples, we illustrate the influence of the kernel scale and show that the method achieves the important property of sparsity across scales. In addition, we demonstrate on a dataset of annotated lung CT images how the kernel bundle framework with a compact representation reaches the same accuracy as the standard method optimally tuned with respect to scale.

AB - In order to detect small-scale deformations during disease propagation while allowing large-scale deformation needed for inter-subject registration, we wish to model deformation at multiple scales and represent the deformation compactly at the relevant scales only. This paper presents the kernel bundle extension of the LDDMM framework that allows multiple kernels at multiple scales to be incorporated in the registration. We combine sparsity priors with the kernel bundle resulting in compact representations across scales, and we present the mathematical foundation of the framework with derivation of the KB-EPDiff evolution equations. Through examples, we illustrate the influence of the kernel scale and show that the method achieves the important property of sparsity across scales. In addition, we demonstrate on a dataset of annotated lung CT images how the kernel bundle framework with a compact representation reaches the same accuracy as the standard method optimally tuned with respect to scale.

KW - Kernel bundle

KW - LDDKBM

KW - LDDMM

KW - Diffeomorphic registration

KW - Scale space

KW - Computational anatomy

U2 - 10.1007/s10851-012-0409-0

DO - 10.1007/s10851-012-0409-0

M3 - Journal article

SN - 0924-9907

VL - 46

SP - 292

EP - 308

JO - Journal of Mathematical Imaging and Vision

JF - Journal of Mathematical Imaging and Vision

IS - 3

ER -