Rapid surface registration of 3D volumes using a neural network approach

Authors:
J. Zhang;Y. Ge;S. H. Ong;C. K. Chui;S. H. Teoh;C. H. Yan
Affiliations:
Department of Electrical and Computer Engineering, National University of Singapore, Block E4-05-48, 4 Engineering Drive 3, Singapore 117576, Singapore and Department of Mechanical Engineering, Na ...;Department of Electrical and Computer Engineering, National University of Singapore, Block E4-05-48, 4 Engineering Drive 3, Singapore 117576, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, Block E4-05-48, 4 Engineering Drive 3, Singapore 117576, Singapore and Division of Bioengineering, National Uni ...;Department of Mechanical Engineering, National University of Singapore, Block EA-07-08, 9 Engineering Drive 1, Singapore 117576, Singapore;Department of Mechanical Engineering, National University of Singapore, Block EA-07-08, 9 Engineering Drive 1, Singapore 117576, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, Block E4-05-48, 4 Engineering Drive 3, Singapore 117576, Singapore
Venue:
Image and Vision Computing
Year:
2008

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Abstract

An automatic surface-based rigid registration system using a neural network representation is proposed. The system has been applied to register human bone structures for image-guided surgery. A multilayer perceptron neural network is used to construct a patient-specific surface model from pre-operative images. A surface representation function derived from the resultant neural network model is then employed for intra-operative registration. The optimal transformation parameters are obtained via an optimization process. This segmentation/registration system achieves sub-voxel accuracy comparable to that of conventional techniques, and is significantly faster. These advantages are demonstrated using image datasets of the calcaneus and vertebrae.