Registration of video images to tomographic images by optimising mutual information using texture mapping

TY - CHAP

T1 - Registration of video images to tomographic images by optimising mutual information using texture mapping

AU - Clarkson, M J

AU - Rueckert, D

AU - King, A P

AU - Edwards, P J

AU - Hill, D L G

AU - Hawkes, D J

PY - 1999

Y1 - 1999

N2 - In this paper we propose a novel tracking method to update the pose of stereo video cameras with respect to a surface model derived from a 3D tomographic image. This has a number of applications in image guided interventions and therapy. Registration of 2D video images to the pre-operative 3D image provides a mapping between image and physical space and enables a perspective projection of the pre-operative data to be overlaid onto the video image. Assuming an initial registration can be achieved, we propose a method for updating the registration, which is based on image intensity and texture mapping. We performed five experiments on simulated, phantom and volunteer data and validated the algorithm against an accurate gold standard in all three cases. We measured the mean 3D error of our tracking algorithm to be 1.05 mm for the simulation and 1.89 mm for the volunteer data. Visually this corresponds to a good registration.

AB - In this paper we propose a novel tracking method to update the pose of stereo video cameras with respect to a surface model derived from a 3D tomographic image. This has a number of applications in image guided interventions and therapy. Registration of 2D video images to the pre-operative 3D image provides a mapping between image and physical space and enables a perspective projection of the pre-operative data to be overlaid onto the video image. Assuming an initial registration can be achieved, we propose a method for updating the registration, which is based on image intensity and texture mapping. We performed five experiments on simulated, phantom and volunteer data and validated the algorithm against an accurate gold standard in all three cases. We measured the mean 3D error of our tracking algorithm to be 1.05 mm for the simulation and 1.89 mm for the volunteer data. Visually this corresponds to a good registration.

U2 - 10.1007/10704282_62

DO - 10.1007/10704282_62

M3 - Conference paper

SN - 9783540665038

T3 - LECTURE NOTES IN COMPUTER SCIENCE

SP - 579

EP - 588

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI'99

A2 - Taylor, Chris

A2 - Colchester, Alain

PB - Springer

CY - BERLIN

T2 - 2nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 99)

Y2 - 1 January 1999

ER -