Multi-modal deformable medical image registration

Fookes, Clinton & Sridharan, Sridha (2008) Multi-modal deformable medical image registration. In 2008 2nd International Conference on Signal Processing and Communication Systems, DSP for Communication Systems / IEEE, Australia, NSW, Gold Coast, pp. 1-9.

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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.

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ID Code: 79993
Item Type: Conference Paper
Refereed: Yes
Keywords: Markov processes, Monte Carlo methods, image registration, medical image processing, Gaussian convolution, grid point selection, hybrid fluid technique, multimodal deformable medical image registration, reversible jump Markov Chain Monte Carlo statistical procedure, sparse deformation field, spline warp, Biomedical imaging
DOI: 10.1109/ICSPCS.2008.4813756
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 12 Jan 2015 02:22
Last Modified: 27 Mar 2015 03:38

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