TY - JOUR
T1 - Curvature based image registration
AU - Fischer, Bernd
AU - Modersitzki, Jan
PY - 2003/1/1
Y1 - 2003/1/1
N2 - A fully automated, non-rigid image registration algorithm is presented. The deformation field is found by minimizing a suitable measure subject to a curvature based constraint. It is a well-known fact that non-rigid image registration techniques may converge poorly if the initial position is not sufficiently near to the solution. A common approach to address this problem is to perform a time consuming rigid pre-registration step. In this paper we show that the new curvature registration not only produces accurate and smooth solutions but also allows for an automatic rigid alignment. Thus, in contrast to other popular registration schemes, the new method no longer requires a pre-registration step. Furthermore, we present an implementation of the new scheme based on the numerical solution of the underlying Euler-Lagrange equations. The real discrete cosine transform is the backbone of our implementation and leads to a stable and fast O(N log N) algorithm, where N denotes the number of voxels. Finally, we report on some numerical test runs.
AB - A fully automated, non-rigid image registration algorithm is presented. The deformation field is found by minimizing a suitable measure subject to a curvature based constraint. It is a well-known fact that non-rigid image registration techniques may converge poorly if the initial position is not sufficiently near to the solution. A common approach to address this problem is to perform a time consuming rigid pre-registration step. In this paper we show that the new curvature registration not only produces accurate and smooth solutions but also allows for an automatic rigid alignment. Thus, in contrast to other popular registration schemes, the new method no longer requires a pre-registration step. Furthermore, we present an implementation of the new scheme based on the numerical solution of the underlying Euler-Lagrange equations. The real discrete cosine transform is the backbone of our implementation and leads to a stable and fast O(N log N) algorithm, where N denotes the number of voxels. Finally, we report on some numerical test runs.
UR - http://www.scopus.com/inward/record.url?scp=0037272745&partnerID=8YFLogxK
U2 - 10.1023/A:1021897212261
DO - 10.1023/A:1021897212261
M3 - Journal articles
AN - SCOPUS:0037272745
SN - 0924-9907
VL - 18
SP - 81
EP - 85
JO - Journal of Mathematical Imaging and Vision
JF - Journal of Mathematical Imaging and Vision
IS - 1
ER -