TY - JOUR
T1 - Registration of medical images using an interpolated closest point transform
T2 - Medical Imaging 2003: Image Processing
AU - Cao, Zhujiang
AU - Pan, Shiyan
AU - Li, Rui
AU - Balachandran, Ramya
AU - Fitzpatrick, Michael J.
AU - Chapman, William C.
AU - Dawant, Benoit M.
N1 - Funding Information:
An early version of this manuscript has been presented at the 2003 SPIE medical imaging conference. This work has been supported, in parts, by NIH grant CA-91352 & R01-CS89323-01. The RREP data set was provided as part of the project, “Retrospective Image Registration Evaluation”, NIH grant 8R01EB002124-03. The authors also thank the anonymous reviewers for their constructive comments.
PY - 2003
Y1 - 2003
N2 - Image registration is an important procedure for medical diagnosis. Since the large inter-site retrospective validation study led by Fitzpatrick at Vanderbilt University, voxel-based methods and more specifically mutual information (MI) based registration methods have been regarded as the method of choice for rigid-body intra-subject registration problems. In this study we propose a method that is based on the iterative closest point (ICP) algorithm and a pre-computed closest point map obtained with a slight modification of the fast marching method proposed by Sethian. We also propose an interpolation scheme that allows us to find the corresponding points with a sub-voxel accuracy even though the closest point map is defined on a regular grid. The method has been tested both on synthetic and real images and registration results have been assessed quantitatively using the data set provided by the Retrospective Registration Evaluation Project. For these volumes, MR and CT head surfaces were extracted automatically using a level-set technique. Results show that on these data sets this registration method leads to accuracy numbers that are comparable to those obtained with voxel-based methods.
AB - Image registration is an important procedure for medical diagnosis. Since the large inter-site retrospective validation study led by Fitzpatrick at Vanderbilt University, voxel-based methods and more specifically mutual information (MI) based registration methods have been regarded as the method of choice for rigid-body intra-subject registration problems. In this study we propose a method that is based on the iterative closest point (ICP) algorithm and a pre-computed closest point map obtained with a slight modification of the fast marching method proposed by Sethian. We also propose an interpolation scheme that allows us to find the corresponding points with a sub-voxel accuracy even though the closest point map is defined on a regular grid. The method has been tested both on synthetic and real images and registration results have been assessed quantitatively using the data set provided by the Retrospective Registration Evaluation Project. For these volumes, MR and CT head surfaces were extracted automatically using a level-set technique. Results show that on these data sets this registration method leads to accuracy numbers that are comparable to those obtained with voxel-based methods.
KW - Closest feature transform
KW - Distance transform
KW - Fast marching method
KW - Surface-based registration
UR - http://www.scopus.com/inward/record.url?scp=0041374130&partnerID=8YFLogxK
U2 - 10.1117/12.480306
DO - 10.1117/12.480306
M3 - Conference article
AN - SCOPUS:0041374130
SN - 0277-786X
VL - 5032 I
SP - 325
EP - 333
JO - Proceedings of SPIE - The International Society for Optical Engineering
JF - Proceedings of SPIE - The International Society for Optical Engineering
Y2 - 17 February 2003 through 20 February 2003
ER -