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http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7078Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.creator | Zollei, Lilla | - |
| dc.date | 2004-10-20T20:28:33Z | - |
| dc.date | 2004-10-20T20:28:33Z | - |
| dc.date | 2001-08-01 | - |
| dc.date.accessioned | 2013-10-09T02:48:10Z | - |
| dc.date.available | 2013-10-09T02:48:10Z | - |
| dc.date.issued | 2013-10-09 | - |
| dc.identifier | AITR-2002-001 | - |
| dc.identifier | http://hdl.handle.net/1721.1/7078 | - |
| dc.identifier.uri | http://koha.mediu.edu.my:8181/xmlui/handle/1721 | - |
| dc.description | The registration of pre-operative volumetric datasets to intra- operative two-dimensional images provides an improved way of verifying patient position and medical instrument loca- tion. In applications from orthopedics to neurosurgery, it has a great value in maintaining up-to-date information about changes due to intervention. We propose a mutual information- based registration algorithm to establish the proper align- ment. For optimization purposes, we compare the perfor- mance of the non-gradient Powell method and two slightly di erent versions of a stochastic gradient ascent strategy: one using a sparsely sampled histogramming approach and the other Parzen windowing to carry out probability density approximation. Our main contribution lies in adopting the stochastic ap- proximation scheme successfully applied in 3D-3D registra- tion problems to the 2D-3D scenario, which obviates the need for the generation of full DRRs at each iteration of pose op- timization. This facilitates a considerable savings in compu- tation expense. We also introduce a new probability density estimator for image intensities via sparse histogramming, de- rive gradient estimates for the density measures required by the maximization procedure and introduce the framework for a multiresolution strategy to the problem. Registration results are presented on uoroscopy and CT datasets of a plastic pelvis and a real skull, and on a high-resolution CT- derived simulated dataset of a real skull, a plastic skull, a plastic pelvis and a plastic lumbar spine segment. | - |
| dc.format | 128 p. | - |
| dc.format | 21043480 bytes | - |
| dc.format | 1712245 bytes | - |
| dc.format | application/postscript | - |
| dc.format | application/pdf | - |
| dc.language | en_US | - |
| dc.relation | AITR-2002-001 | - |
| dc.subject | AI | - |
| dc.subject | registration | - |
| dc.subject | medical imaging | - |
| dc.title | 2D-3D Rigid-Body Registration of X-Ray Fluoroscopy and CT Images | - |
| Appears in Collections: | MIT Items | |
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