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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Fookes, C. Sridharan, S. |
| Copyright Year | 2008 |
| Description | Author affiliation: Image & Video Res. Lab., Queensland Univ. of Technol., Brisbane, QLD (Fookes, C.; Sridharan, S.) |
| Abstract | Non-rigid image registration is an essential tool required for overcoming the inherent local anatomical variations that exist between images acquired from different individuals or atlases. Furthermore, certain applications require this type of registration to operate across images acquired from different imaging modalities. One popular local approach for estimating this registration is a block matching procedure utilising the mutual information criterion. However, previous block matching procedures generate a sparse deformation field containing displacement estimates at uniformly spaced locations. This neglects to make use of the evidence that block matching results are dependent on the amount of local information content. This paper presents a solution to this drawback by proposing the use of a Reversible Jump Markov Chain Monte Carlo statistical procedure to optimally select grid points of interest. Three different methods are then compared to propagate the estimated sparse deformation field to the entire image including a thin-plate spline warp, Gaussian convolution, and a hybrid fluid technique. Results show that non-rigid registration can be improved by using the proposed algorithm to optimally select grid points of interest. |
| Starting Page | 1 |
| Ending Page | 9 |
| File Size | 290917 |
| Page Count | 9 |
| File Format | |
| ISBN | 9781424442430 |
| DOI | 10.1109/ICSPCS.2008.4813756 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-12-15 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image registration Image segmentation Monte Carlo methods Convolution Laboratories Humans Optimal control Spline Mutual information Biomedical imaging |
| Content Type | Text |
| Resource Type | Article |
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