Locally orderless registration for diffusion weighted images

TY - GEN

T1 - Locally orderless registration for diffusion weighted images

AU - Jensen, Henrik Grønholt

AU - Lauze, Francois Bernard

AU - Nielsen, Mads

AU - Darkner, Sune

N1 - Conference code: 18

PY - 2015

Y1 - 2015

N2 - Registration of Diffusion Weighted Images (DWI) is challenging as the data, in contrast to scalar-valued images, is a composition of both directional and intensity information. The DWI signal is known to be influenced by noise and a wide range of artifacts. Therefore, it is attractive to use similarity measures with invariance properties, such as Mutual Information. However, density estimation from DWI is complicated by directional information. We address this problem by extending Locally Orderless Registration (LOR), a density estimation framework for image similarity, to include directional information. We construct a spatio-directional scale-space formulation of marginal and joint density distributions between two DWI, that takes the projective nature of the directional information into account. This accounts for orientation and magnitude and enables us to use a wide range of similarity measures from the LOR framework. Using Mutual Information, we examine the properties of the scale-space induced by the choice of kernels and illustrate the approach by affine registration.

AB - Registration of Diffusion Weighted Images (DWI) is challenging as the data, in contrast to scalar-valued images, is a composition of both directional and intensity information. The DWI signal is known to be influenced by noise and a wide range of artifacts. Therefore, it is attractive to use similarity measures with invariance properties, such as Mutual Information. However, density estimation from DWI is complicated by directional information. We address this problem by extending Locally Orderless Registration (LOR), a density estimation framework for image similarity, to include directional information. We construct a spatio-directional scale-space formulation of marginal and joint density distributions between two DWI, that takes the projective nature of the directional information into account. This accounts for orientation and magnitude and enables us to use a wide range of similarity measures from the LOR framework. Using Mutual Information, we examine the properties of the scale-space induced by the choice of kernels and illustrate the approach by affine registration.

U2 - 10.1007/978-3-319-24571-3_37

DO - 10.1007/978-3-319-24571-3_37

M3 - Article in proceedings

SN - 978-3-319-24570-6

T3 - Lecture notes in computer science

SP - 305

EP - 312

BT - Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015

A2 - Navab, Nassir

A2 - Hornegger, Joachim

A2 - Wells, William M.

A2 - Frangi, Alejandro F.

PB - Springer

T2 - International Conference on Medical Image Computing and Computer Assisted Intervention 2015

Y2 - 5 October 2015 through 9 October 2015

ER -