Please use this identifier to cite or link to this item:
https://doi.org/10.1016/j.imavis.2007.04.003
DC Field | Value | |
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dc.title | Rapid surface registration of 3D volumes using a neural network approach | |
dc.contributor.author | Zhang, J. | |
dc.contributor.author | Ge, Y. | |
dc.contributor.author | Ong, S.H. | |
dc.contributor.author | Chui, C.K. | |
dc.contributor.author | Teoh, S.H. | |
dc.contributor.author | Yan, C.H. | |
dc.date.accessioned | 2014-04-24T07:24:13Z | |
dc.date.available | 2014-04-24T07:24:13Z | |
dc.date.issued | 2008-02-01 | |
dc.identifier.citation | Zhang, J., Ge, Y., Ong, S.H., Chui, C.K., Teoh, S.H., Yan, C.H. (2008-02-01). Rapid surface registration of 3D volumes using a neural network approach. Image and Vision Computing 26 (2) : 201-210. ScholarBank@NUS Repository. https://doi.org/10.1016/j.imavis.2007.04.003 | |
dc.identifier.issn | 02628856 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/51020 | |
dc.description.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. © 2007 Elsevier B.V. All rights reserved. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.imavis.2007.04.003 | |
dc.source | Scopus | |
dc.subject | Medical images | |
dc.subject | Neural network | |
dc.subject | Surface modelling | |
dc.subject | Surface registration | |
dc.type | Article | |
dc.contributor.department | MECHANICAL ENGINEERING | |
dc.contributor.department | ELECTRICAL & COMPUTER ENGINEERING | |
dc.description.doi | 10.1016/j.imavis.2007.04.003 | |
dc.description.sourcetitle | Image and Vision Computing | |
dc.description.volume | 26 | |
dc.description.issue | 2 | |
dc.description.page | 201-210 | |
dc.description.coden | IVCOD | |
dc.identifier.isiut | 000251849500005 | |
Appears in Collections: | Staff Publications |
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