Distributed Medical Image and Volume Registration

Distributed Medical Image and Volume Registration

Roger Tait (Nottingham Trent University, UK) and Gerald Schaefer (Aston University, UK)
Copyright: © 2008 |Pages: 7
DOI: 10.4018/978-1-59904-889-5.ch059
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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.

Key Terms in this Chapter

Distributed Agents: Software entities designed to execute as independent threads and on distributed processors, capable of acting autonomously in order to achieve a predefined task.

Blackboard Architecture: An artificial intelligence application based on and analogous to a group of experts seated in a room with a large blackboard working as a team to solve a common problem.

Coarse-Grained Parallelism: A term used to describe an algorithm that has been divided into high-level components, each of which can be hosted by a separate processor.

Image Registration: The process whereby two images, differing by a spatial transformation, are brought into geometric alignment.

Parallel Processing: The concurrent execution of the same task, split into components, on multiple processors in order to achieve faster processing speeds.

Similarity Metric: A measure used to quantitatively judge how well a transformed sensed (moving) image fits a reference (fixed) image by comparing intensities.

Fine-Grained Parallelism: A term used to describe an algorithm that has been divided into low-level components, each of which can be hosted by a separate processor.

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