IEICE TRANSACTIONS on Information
Automatic 3D MR Image Registration and Its Evaluation for Precise Monitoring of Knee Joint Disease
Summary :
We describe a technique for the registration of three dimensional (3D) knee femur surface points from MR image data sets; it is a technique that can track local cartilage thickness changes over time. In the first coarse registration step, we use the direction vectors of the volume given by the cloud of points of the MR image to correct for different knee joint positions and orientations in the MR scanner. In the second fine registration step, we propose a global search algorithm that simultaneously determines the optimal transformation parameters and point correspondences through searching a six dimensional space of Euclidean motion vectors (translation and rotation). The present algorithm is grounded on a mathematical theory - Lipschitz optimization. Compared with the other three registration approaches (ICP, EM-ICP, and genetic algorithms), the proposed method achieved the highest registration accuracy on both animal and clinical data.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.3 pp.698-706
- Publication Date
- 2011/03/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.698
- Type of Manuscript
- PAPER
- Category
- Biological Engineering
Authors
Keyword
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Yuanzhi CHENG, Quan JIN, Hisashi TANAKA, Changyong GUO, Xiaohua DING, Shinichi TAMURA, "Automatic 3D MR Image Registration and Its Evaluation for Precise Monitoring of Knee Joint Disease" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 3, pp. 698-706, March 2011, doi: 10.1587/transinf.E94.D.698.
Abstract: We describe a technique for the registration of three dimensional (3D) knee femur surface points from MR image data sets; it is a technique that can track local cartilage thickness changes over time. In the first coarse registration step, we use the direction vectors of the volume given by the cloud of points of the MR image to correct for different knee joint positions and orientations in the MR scanner. In the second fine registration step, we propose a global search algorithm that simultaneously determines the optimal transformation parameters and point correspondences through searching a six dimensional space of Euclidean motion vectors (translation and rotation). The present algorithm is grounded on a mathematical theory - Lipschitz optimization. Compared with the other three registration approaches (ICP, EM-ICP, and genetic algorithms), the proposed method achieved the highest registration accuracy on both animal and clinical data.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.698/_p
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@ARTICLE{e94-d_3_698,
author={Yuanzhi CHENG, Quan JIN, Hisashi TANAKA, Changyong GUO, Xiaohua DING, Shinichi TAMURA, },
journal={IEICE TRANSACTIONS on Information},
title={Automatic 3D MR Image Registration and Its Evaluation for Precise Monitoring of Knee Joint Disease},
year={2011},
volume={E94-D},
number={3},
pages={698-706},
abstract={We describe a technique for the registration of three dimensional (3D) knee femur surface points from MR image data sets; it is a technique that can track local cartilage thickness changes over time. In the first coarse registration step, we use the direction vectors of the volume given by the cloud of points of the MR image to correct for different knee joint positions and orientations in the MR scanner. In the second fine registration step, we propose a global search algorithm that simultaneously determines the optimal transformation parameters and point correspondences through searching a six dimensional space of Euclidean motion vectors (translation and rotation). The present algorithm is grounded on a mathematical theory - Lipschitz optimization. Compared with the other three registration approaches (ICP, EM-ICP, and genetic algorithms), the proposed method achieved the highest registration accuracy on both animal and clinical data.},
keywords={},
doi={10.1587/transinf.E94.D.698},
ISSN={1745-1361},
month={March},}
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TY - JOUR
TI - Automatic 3D MR Image Registration and Its Evaluation for Precise Monitoring of Knee Joint Disease
T2 - IEICE TRANSACTIONS on Information
SP - 698
EP - 706
AU - Yuanzhi CHENG
AU - Quan JIN
AU - Hisashi TANAKA
AU - Changyong GUO
AU - Xiaohua DING
AU - Shinichi TAMURA
PY - 2011
DO - 10.1587/transinf.E94.D.698
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2011
AB - We describe a technique for the registration of three dimensional (3D) knee femur surface points from MR image data sets; it is a technique that can track local cartilage thickness changes over time. In the first coarse registration step, we use the direction vectors of the volume given by the cloud of points of the MR image to correct for different knee joint positions and orientations in the MR scanner. In the second fine registration step, we propose a global search algorithm that simultaneously determines the optimal transformation parameters and point correspondences through searching a six dimensional space of Euclidean motion vectors (translation and rotation). The present algorithm is grounded on a mathematical theory - Lipschitz optimization. Compared with the other three registration approaches (ICP, EM-ICP, and genetic algorithms), the proposed method achieved the highest registration accuracy on both animal and clinical data.
ER -
English
