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Distributed Medical Image and Volume Registration
Abstract
The ability to visualise hidden structures in detail using 3-D volume data has become a valuable resource in medical imaging applications (Maintz & Viergever, 1998). Importantly, the alignment of volumes enables the combination of different structural and functional information for diagnosis and planning purposes (Pluim, Maintz, & Viergever, 2003). Transform optimisation, resampling, and similarity calculation form the basic stages of a registration process (Zitova & Flusser, 2003): During transform optimisation, translation and rotation parameters which geometrically map points in the reference (fixed) image/volume to points in the sensed (moving) image/volume are estimated. Once estimated, pixel/voxel intensities which are mapped into nondiscrete coordinates are interpolated during the resampling stage. After resampling, a metric is used for similarity calculation in which the degree of likeness between corresponding volumes is evaluated (Tait & Schaefer, 2008). Optimisation of the similarity measure is the goal of the registration process and is achieved by seeking the best transform. All possible transform parameters therefore define the search space. Due to the iterative nature of registration algorithms, similarity calculation represents a considerable performance bottleneck which limits the speed of time critical clinical applications.
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